Autonomous Driving Platform Performance Analysis

Through data analysis of various plots and figures it will be possible to determine the best control parameters to get the best performance out of the autonomous driving platform. This data, presented in this thesis, will show quantitatively what the best control strategies are through comparison of different versions of the platform

Urgent need for protection of New Zealand’s coastal landscape

Alarm bells for protection of coastal landscape should be well and truly ringing! This is exemplified by the great rush toward “life-style block” subdivision of large coastal tracts (simply glance at the “NZ Herald” Real Estate section), and recent cases such as the University of Auckland’s hedonistic attempt to sell prime coastal land on the scenic Coromandel coastline for development. Coastal landscape protection is already embedded in the Resource Management Act, and most explicitly stated under S6 (“Matters of National Importance”). S6a refers to “preservation of the natural character of the coastal environment” – which implicitly includes landscape - and S6b “the protection of outstanding natural features and landscapes from inappropriate subdivision….”. Unfortunately landscape protection is rarely considered seriously as a major impediment to new sub-divisional developments along areas of largely undeveloped coast. There are compelling reasons for protection of coastal landscape. These include (i) reduction in long term economic return from tourism from ribbon development along the coast, (ii) huge increases in the cost of supplying infrastructure (roading, electricity, water supply, sewage disposal) to remote coastal wild and scenic locations – which the entire community contributes major cost for rather than the select few beneficiaries at the end of the line; and (iii) the improved infrastructure amenities, facilities and economic benefits possible from concentration of capital development into nucleated coastal settlements. But the major problem is the creeping ribbon development along the coast – leading to significant irreversible impact on the “vistas of nature” – especially along the scenic coasts of Northland, the Coromandel Peninsula, the central North Island and the Marlborough Sounds

Investigation of image enhancement techniques for the development of a self-contained airborne radar navigation system

This study was devoted to an investigation of the feasibility of applying advanced image processing techniques to enhance radar image characteristics that are pertinent to the pilot's navigation and guidance task. Millimeter (95 GHz) wave radar images for the overwater (i.e., offshore oil rigs) and overland (Heliport) scenario were used as a data base. The purpose of the study was to determine the applicability of image enhancement and scene analysis algorithms to detect and improve target characteristics (i.e., manmade objects such as buildings, parking lots, cars, roads, helicopters, towers, landing pads, etc.) that would be helpful to the pilot in determining his own position/orientation with respect to the outside world and assist him in the navigation task. Results of this study show that significant improvements in the raw radar image may be obtained using two dimensional image processing algorithms. In the overwater case, it is possible to remove the ocean clutter by thresholding the image data, and furthermore to extract the target boundary as well as the tower and catwalk locations using noise cleaning (e.g., median filter) and edge detection (e.g., Sobel operator) algorithms

Using Interactive Maps in Community Applications

Interactive maps provide unique ways to support community applications. In particular, they enable new collaborative activities. Map-based navigation supports a community environment as well as virtual tours. Interactive maps can also function as a tool in collecting historical information and discussing new spatial layouts. These examples indicate the numerous opportunities for interactive maps to support collaboration

The Effects of Flood Warning Information on Driver Decisions in a Driving Simulator Scenario

Flood warnings are a type of risk communication that alerts the public of potential floods. Flood warnings can be communicated through mobile devices and should convey enough information to keep the user safe during a flood situation. However, the amount of detail included in the warning, such as the depth of the flood, may vary. The purpose of this study was to: (a) extend our prior research on flood warnings by recreating the written driving scenarios into the driving simulator; (b) deepen the understanding of human decision-making in risky situations; and (c) investigate how to best inform drivers of floods by design to keep them protected. We examined the effects of flood warning information on the actions taken by drivers in various driving scenarios in a driving simulator. Participants were tasked to drive to a restaurant after receiving instructions and a type of flood information warning during each scenario (flood, no flood, flood of 6 inches, flood of 6 inches maximum). Their actions taken, trust in the navigation system, understanding of the situation and scenario, and perceived risk were measured for each type of flood information warning. We found that participants accepted the alternate route more when in a scenario with a flood present compared to the no-flood scenario. The level of detail of the warning did not influence the actions taken. These results deepened the understanding of human decision-making and can guide future flood warning designs to keep drivers protected from flooded roadways

Virtual-to-Real-World Transfer Learning for Robots on Wilderness Trails

Robots hold promise in many scenarios involving outdoor use, such as search-and-rescue, wildlife management, and collecting data to improve environment, climate, and weather forecasting. However, autonomous navigation of outdoor trails remains a challenging problem. Recent work has sought to address this issue using deep learning. Although this approach has achieved state-of-the-art results, the deep learning paradigm may be limited due to a reliance on large amounts of annotated training data. Collecting and curating training datasets may not be feasible or practical in many situations, especially as trail conditions may change due to seasonal weather variations, storms, and natural erosion. In this paper, we explore an approach to address this issue through virtual-to-real-world transfer learning using a variety of deep learning models trained to classify the direction of a trail in an image. Our approach utilizes synthetic data gathered from virtual environments for model training, bypassing the need to collect a large amount of real images of the outdoors. We validate our approach in three main ways. First, we demonstrate that our models achieve classification accuracies upwards of 95% on our synthetic data set. Next, we utilize our classification models in the control system of a simulated robot to demonstrate feasibility. Finally, we evaluate our models on real-world trail data and demonstrate the potential of virtual-to-real-world transfer learning.Comment: iROS 201