DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car

We present DeepPicar, a low-cost deep neural network based autonomous car platform. DeepPicar is a small scale replication of a real self-driving car called DAVE-2 by NVIDIA. DAVE-2 uses a deep convolutional neural network (CNN), which takes images from a front-facing camera as input and produces car steering angles as output. DeepPicar uses the same network architecture---9 layers, 27 million connections and 250K parameters---and can drive itself in real-time using a web camera and a Raspberry Pi 3 quad-core platform. Using DeepPicar, we analyze the Pi 3's computing capabilities to support end-to-end deep learning based real-time control of autonomous vehicles. We also systematically compare other contemporary embedded computing platforms using the DeepPicar's CNN-based real-time control workload. We find that all tested platforms, including the Pi 3, are capable of supporting the CNN-based real-time control, from 20 Hz up to 100 Hz, depending on hardware platform. However, we find that shared resource contention remains an important issue that must be considered in applying CNN models on shared memory based embedded computing platforms; we observe up to 11.6X execution time increase in the CNN based control loop due to shared resource contention. To protect the CNN workload, we also evaluate state-of-the-art cache partitioning and memory bandwidth throttling techniques on the Pi 3. We find that cache partitioning is ineffective, while memory bandwidth throttling is an effective solution.Comment: To be published as a conference paper at RTCSA 201

Planning for Active Transportation in the Western United States: An Alternative Future for Cache Valley, Utah

Mobility in the western U.S. is defined primarily by the private automobile. Since the conclusion of WWII, the private automobile has become readily available to the public, and as a result, has heavily influenced the design of our modern cities in the west. In recent years the connections between high motor vehicle use and rising obesity rates, crumbling road infrastructure, and deteriorating air quality have caused city officials to reexamine the transportation systems of the west. One solution advocates, city officials, and planning professionals have begun examining is active transportation (walking, cycling, and public transit). Research suggests that a robust active transportation network not only diversifies mobility options, it also encourages compact urban development, cleaner air, and a move active population. This thesis developed a methodology for examining and documenting the components of an active transportation network in the western U.S. This was done though a comprehensive literature review to glean important active transportation policies, infrastructure, and best practices. Then, two western U.S. case study cities with relatively high amounts of cycling, walking, and public transit use were selected and analyzed with site visits and planning professional interviews. The data gathered throughout this first phase of the research was then synthesized, and reoccurring themes about cycling, walking and public transit were identified. These themes were labeled as the prerequisites for active transportation in cities of the western U.S. and were documented and prioritized based on their potential impact. The themes were vetted by planning professionals in the two case study cities as well as in Cache Valley to insure accuracy and validity. A final version of the prerequisites was then documented. The final phase of this research applied the prerequisites to the transportation system in Cache Valley, UT in order to insure the list was valid and reproducible under a variety of conditions. The outcome of this phase was GIS map displaying an alternative future for active transportation in Cache Valley, UT

Physiology-Aware Rural Ambulance Routing

In emergency patient transport from rural medical facility to center tertiary hospital, real-time monitoring of the patient in the ambulance by a physician expert at the tertiary center is crucial. While telemetry healthcare services using mobile networks may enable remote real-time monitoring of transported patients, physiologic measures and tracking are at least as important and requires the existence of high-fidelity communication coverage. However, the wireless networks along the roads especially in rural areas can range from 4G to low-speed 2G, some parts with communication breakage. From a patient care perspective, transport during critical illness can make route selection patient state dependent. Prompt decisions with the relative advantage of a longer more secure bandwidth route versus a shorter, more rapid transport route but with less secure bandwidth must be made. The trade-off between route selection and the quality of wireless communication is an important optimization problem which unfortunately has remained unaddressed by prior work. In this paper, we propose a novel physiology-aware route scheduling approach for emergency ambulance transport of rural patients with acute, high risk diseases in need of continuous remote monitoring. We mathematically model the problem into an NP-hard graph theory problem, and approximate a solution based on a trade-off between communication coverage and shortest path. We profile communication along two major routes in a large rural hospital settings in Illinois, and use the traces to manifest the concept. Further, we design our algorithms and run preliminary experiments for scalability analysis. We believe that our scheduling techniques can become a compelling aid that enables an always-connected remote monitoring system in emergency patient transfer scenarios aimed to prevent morbidity and mortality with early diagnosis treatment.Comment: 6 pages, The Fifth IEEE International Conference on Healthcare Informatics (ICHI 2017), Park City, Utah, 201

A Survey of Procedural Techniques for City Generation

The computer game industry requires a skilled workforce and this combined with the complexity of modern games, means that production costs are extremely high. One of the most time consuming aspects is the creation of game geometry, the virtual world which the players inhabit. Procedural techniques have been used within computer graphics to create natural textures, simulate special effects and generate complex natural models including trees and waterfalls. It is these procedural techniques that we intend to harness to generate geometry and textures suitable for a game situated in an urban environment. Procedural techniques can provide many benefits for computer graphics applications when the correct algorithm is used. An overview of several commonly used procedural techniques including fractals, L-systems, Perlin noise, tiling systems and cellular basis is provided. The function of each technique and the resulting output they create are discussed to better understand their characteristics, benefits and relevance to the city generation problem. City generation is the creation of an urban area which necessitates the creation of buildings, situated along streets and arranged in appropriate patterns. Some research has already taken place into recreating road network patterns and generating buildings that can vary in function and architectural style. We will study the main body of existing research into procedural city generation and provide an overview of their implementations and a critique of their functionality and results. Finally we present areas in which further research into the generation of cities is required and outline our research goals for city generation

An Indicator Based Transportation Sustainability Assessment in Regional Development: A Case Study for Cache County, Utah

Evaluating sustainability for a key system like transportation can be vital for both planners and citizens alike, as planners provide the system and citizens use the system. A sustainable transportation system not only builds a prosperous economy, but it also ensures social equity and a healthy environment for years to come. There are differing scales of sustainability assessment, ranging from neighborhood to global. However, a sustainability scale between the local and national scale is not very common in practice. Therefore, this study offers a regional scale sustainability assessment for the transportation system that will address local changes while also reflecting national requirements. To do the assessment study, regional sustainability indicators for the transportation system have been used for benchmarking within a numerical scale. In the end, the study will provide an aggregated result that will represent the region’s transportation sustainability condition. Such an assessment is akin to evaluating a student based on grades for several subjects

Train-scheduling optimization model for railway networks with multiplatform stations

This paper focuses on optimizing the schedule of trains on railway networks composed of busy complex stations. A mathematical formulation of this problem is provided as a Mixed Integer Linear Program (MILP). However, the creation of an optimal new timetable is an NP-hard problem; therefore, the MILP can be solved for easy cases, computation time being impractical for more complex examples. In these cases, a heuristic approach is provided that makes use of genetic algorithms to find a good solution jointly with heuristic techniques to generate an initial population. The algorithm was applied to a number of problem instances producing feasible, though not optimal, solutions in several seconds on a laptop, and compared to other proposals. Some improvements are suggested to obtain better results and further improve computation time. Rail transport is recognized as a sustainable and energy-efficient means of transport. Moreover, each freight train can take a large number of trucks off the roads, making them safer. Studies in this field can help to make railways more attractive to travelers by reducing operative cost, and increasing the number of services and their punctuality. To improve the transit system and service, it is necessary to build optimal train scheduling. There is an interest from the industry in automating the scheduling process. Fast computerized train scheduling, moreover, can be used to explore the effects of alternative draft timetables, operating policies, station layouts, and random delays or failures.Postprint (published version