Autonomous Vehicles: Evolution of Artificial Intelligence and Learning Algorithms

The advent of autonomous vehicles has heralded a transformative era in transportation, reshaping the landscape of mobility through cutting-edge technologies. Central to this evolution is the integration of Artificial Intelligence (AI) and learning algorithms, propelling vehicles into realms of unprecedented autonomy. This paper provides a comprehensive exploration of the evolutionary trajectory of AI within autonomous vehicles, tracing the journey from foundational principles to the most recent advancements. Commencing with a current landscape overview, the paper delves into the fundamental role of AI in shaping the autonomous decision-making capabilities of vehicles. It elucidates the steps involved in the AI-powered development life cycle in vehicles, addressing ethical considerations and bias in AI-driven software development for autonomous vehicles. The study presents statistical insights into the usage and types of AI/learning algorithms over the years, showcasing the evolving research landscape within the automotive industry. Furthermore, the paper highlights the pivotal role of parameters in refining algorithms for both trucks and cars, facilitating vehicles to adapt, learn, and improve performance over time. It concludes by outlining different levels of autonomy, elucidating the nuanced usage of AI and learning algorithms, and automating key tasks at each level. Additionally, the document discusses the variation in software package sizes across different autonomy levelsComment: 13 page

Nighttime Lights as a Proxy for Economic Performance of Regions

Studying and managing regional economic development in the current globalization era demands prompt, reliable, and comparable estimates for a region’s economic performance. Night-time lights (NTL) emitted from residential areas, entertainment places, industrial facilities, etc., and captured by satellites have become an increasingly recognized proxy for on-ground human activities. Compared to traditional indicators supplied by statistical offices, NTLs may have several advantages. First, NTL data are available all over the world, providing researchers and official bodies with the opportunity to obtain estimates even for regions with extremely poor reporting practices. Second, in contrast to non-standardized traditional reporting procedures, the unified NTL data remove the problem of inter-regional comparability. Finally, NTL data are currently globally available on a daily basis, which makes it possible to obtain these estimates promptly. In this book, we provide the reader with the contributions demonstrating the potential and efficiency of using NTL data as a proxy for the performance of regions

Liberated pixels : alternative narratives for lighting future cities

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 165-171).Lighting and illuminated displays shape our relations to urban environments and to one another at night and increasingly during the day by transforming what Kevin Lynch referred to as the "image of the city" (1964). Today, the wide-spread availability of LEDs (light-emitting diodes) in combination with embedded, miniaturized computation offers different ways of designing ambient infrastructures. In this dissertation, I explore these alternatives by exploiting the programmable and responsive capabilities of LED-based, low-resolution systems. In short, I examine the alternative aesthetic and communications opportunities afforded by a new generation of lighting and display technologies in the city. I investigate the origins of lighting and displays to illustrate how they have evolved through a complex interleaving of the social and the material. This grounding leads me to develop three design explorations that focus on programmability, addressability, responsiveness, mobility and ad-hoc control. The first of these explorations, Urban Pixels, presents a wireless network of individual, autonomous physical pixels that can be deployed on any surface in the city. The second, Light Bodies, reconnects with the history of lights-on-people like lanterns that travel through the city with their users. The third, Augmented-reality (AR) Street Light, provides a layer of programmability for existing infrastructural networks. Together the historical perspective and design interventions lead to a framework of what I call "liberated pixels", a new generation of lighting and display technologies. Liberated pixels can be placed flexibly within any context and recruited in different situations for aesthetic and ambient information purposes. This vision captures the contingent and emergent nature of "sociomaterial assemblages" (Suchman 2007) to chart holistic technical, aesthetic, and social directions for future infrastructures of "imageability" (Lynch 1964) in the city.by Susanne SeitingerPh.D

Using Micrometeorology to Gauge Agriculture\u27s Potential to Sequester Soil Carbon

In addition to reducing carbon dioxide (CO2) emissions from fossil fuel combustion, removing atmospheric CO2 may be critical to limit global warming to less than two degrees Celsius above pre-industrial levels recommended by leading experts. Since cropland occupies 11% of the earth’s land and is intensively managed, cropland agriculture provides one approach for removing CO2 from the atmosphere to mitigate climate change. However, current assessments indicate agriculture is a net emitter of CO2 and other greenhouse gases, and it is unclear how soil management can effect carbon sequestration.In this work micrometeorological methods are used to measure the exchange (flux) of CO2 between the surface and atmosphere and can assess whether an agricultural ecosystem is a source or sink for carbon. Three studies were performed using micrometeorology to understand agriculture’s potential to sequester carbon.Using Bowen Ratio Energy Balance (BREB) micrometeorological methods, the first study measured CO2 flux from a maize crop grown on no-till and tilled soils to determine tillage effects on CO2 emissions during 104 days of the 2015 maize growing season in north central Ohio. During this period, the no-till plot sequestered CO2, while the tilled plot was a net emitter.A second study determined if industrial biotechnology waste reutilization in agriculture could reduce CO2 emissions and generate environmental benefits, while meeting farmer yield expectations. Using both BREB and eddy covariance (EC) micrometeorological methods, CO2 flux was measured over maize where heat-inactivated, spent microbial biomass (SMB) amendment was land applied and compared with typical farmer practices from October 2016 to October 2017 in Loudon, Tennessee. While treatments with SMB emitted more CO2 than farmer practices, the SMB applications produced yields similar to farmer practices.Using BREB micrometeorology methods, the third study measured CO2 emissions over conservation agriculture (CA) practices as compared to conventional tillage from June 2013 to May 2016 in central Zimbabwe. The CA practices of no-till and cover crops produced significantly fewer CO2 emissions than conventional tillage.These studies demonstrate that micrometeorology can detect short- and long-term differences in CO2 flux between practices, providing data supporting agriculture’s potential to reduce CO2 emissions and sequester carbon

Evaluating Impacts of Shared E-scooters from the Lens of Sustainable Transportation

As the popularity of shared micromobility is increasing worldwide, city governments are struggling to regulate and manage these innovative travel technologies that have several benefits, including increasing accessibility, reducing emissions, and providing affordable travel options. This dissertation evaluates the impacts of shared micromobility from the perspective of sustainable transportation to provide recommendations to decision-makers, planners, and engineers for improving these emerging travel technologies. The dissertation focuses on four core aspects of shared micromobility as follows: 1) Safety: I evaluated police crash reports of motor vehicle involving e-scooter and bicycle crashes using the most recent PBCAT crash typology to provide a comprehensive picture of demographics of riders crashing and crash characteristics, as well as mechanism of crash and crash risk, 2) Economics: I estimated the demand elasticity of e-scooters deployed, segmented by weekday type, land use, category of service providers based on fleet size using negative binomial fixed effect regression model and K-means clustering, 3) Expanding micromobility to emerging economies: Using dynamic stated preference pivoting survey and panel data mixed logit model, I assessed the intentions to adopt shared micromobility in mid-sized cities of developing countries, where these innovative technology could be the first wave of decarbonizing transportation sector, and 4) Micromobility data application: I identified five usage-clusters of shared e-scooter trips using combination of Principal Component Analysis (PCA) and K-means clustering to propose a novel framework for using micromobility data to inform data-driven decision on broader policy goals. Based on the key findings of the research, I provide five recommendations as follows: 1) decision-makers should be proactive in incorporating new travel technologies like shared micromobility, 2) city governments should leverage shared micromobility usage and operation data to empower the decision-making process, 3) each shared micromobility vehicles should be approached uniquely for improving road safety, 4) city governments should consider regulating the number of service providers and their fleet sizes, and 5) decision-makers should prioritize expanding shared micromobility in emerging economies as one of the first efforts to the decarbonizing transportation sector

Synthetic Data for Machine Learning

Supervised machine learning methods require large-scale training datasets to converge. Collecting and annotating training data is expensive, time-consuming, error-prone, and not always practical. Usually, synthetic data is used as a feasible data source to increase the amount of training data. However, just directly using synthetic data may actually harm the model’s performance or may not be as effective as it could be. This thesis addresses the challenges of generating large-scale synthetic data, improving domain adaptation in semantic segmentation, advancing video stabilization in adverse conditions, and conducting a rigorous assessment of synthetic data usability in classification tasks. By contributing novel solutions to these multifaceted problems, this work bolsters the field of computer vision, offering strong foundations for a broad range of applications for utilizing synthetic data for computer vision tasks. In this thesis, we divide the study into three main problems: (i) Tackle the problem of generating diverse and photorealistic synthetic data; (ii) Explore synthetic-aware computer vision solutions for semantic segmentation and video stabilization; (iii) Assess the usability of synthetically generated data for different computer vision tasks. We developed a new synthetic data generator called Silver. Photo-realism, diversity, scalability, and full 3D virtual world generation at run-time are the key aspects of this generator. The photo-realism was approached by utilizing the stateof-the-art High Definition Render Pipeline (HDRP) of the Unity game engine. In parallel, the Procedural Content Generation (PCG) concept was employed to create a full 3D virtual world at run-time, while the scalability (expansion and adaptability) of the system was attained by taking advantage of the modular approach followed as we built the system from scratch. Silver can be used to provide clean, unbiased, and large-scale training and testing data for various computer vision tasks. Regarding synthetic-aware computer vision models, we developed a novel architecture specifically designed to use synthetic training data for semantic segmentation domain adaptation. We propose a simple yet powerful addition to DeepLabV3+ by using weather and time-of-the-day supervisors trained with multitask learning, making it both weather and nighttime-aware, which improves its mIoU accuracy under adverse conditions while maintaining adequate performance under standard conditions. Similarly, we also proposed a synthetic-aware adverse weather video stabilization algorithm that dispenses real data for training, relying solely on synthetic data. Our approach leverages specially generated synthetic data to avoid the feature extraction issues faced by current methods. To achieve this, we leveraged our novel data generator to produce the required training data with an automatic ground-truth extraction procedure. We also propose a new dataset called VSAC105Real and compare our method to five recent video stabilization algorithms using two benchmarks. Our method generalizes well on real-world videos across all weather conditions and does not require large-scale synthetic training data. Finally, we assess the usability of the generated synthetic data. We propose a novel usability metric that disentangles photorealism from diversity. This new metric is a simple yet effective way to rank synthetic images. The quantitative results show that we can achieve similar or better results by training on 50% less synthetic data. Additionally, we qualitatively assess the impact of photorealism and evaluate many architectures on different datasets for that aim

A Robust Vehicle Detection model for LiDAR sensor using Simulation Data and Transfer Learning Methods

Vehicle detection in parking areas provides the spatial and temporal utilisation of parking spaces. Parking observations are typically performed manually, limiting the temporal resolution due to the high labour cost. This paper uses simulated data and transfer learning to build a robust real-world model for vehicle detection and classification from single-beam LiDAR of a roadside parking scenario. The paper presents a synthetically augmented transfer learning approach for LiDAR-based vehicle detection and the implementation of synthetic LiDAR data. A synthetic augmented transfer learning method was used to supplement the small real-world data set and allow the development of data-handling techniques. In addition, adding the synthetically augmented transfer learning method increases the robustness and overall accuracy of the model. Experiments show that the method can be used for fast deployment of the model for vehicle detection using a LIDAR sensor