COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference

The process of automatic generation of a road map from GPS trajectories, called map inference, remains a challenging task to perform on a geospatial data from a variety of domains as the majority of existing studies focus on road maps in cities. Inherently, existing algorithms are not guaranteed to work on unusual geospatial sites, such as an airport tarmac, pedestrianized paths and shortcuts, or animal migration routes, etc. Moreover, deep learning has not been explored well enough for such tasks. This paper introduces COLTRANE, ConvolutiOnaL TRAjectory NEtwork, a novel deep map inference framework which operates on GPS trajectories collected in various environments. This framework includes an Iterated Trajectory Mean Shift (ITMS) module to localize road centerlines, which copes with noisy GPS data points. Convolutional Neural Network trained on our novel trajectory descriptor is then introduced into our framework to detect and accurately classify junctions for refinement of the road maps. COLTRANE yields up to 37% improvement in F1 scores over existing methods on two distinct real-world datasets: city roads and airport tarmac.Comment: BuildSys 201

Knowledge Discovery and Data Mining for Shared Mobility and Connected and Automated Vehicle Applications

The rapid development of shared mobility and connected and automated vehicles (CAVs) has not only brought new intelligent transportation system (ITS) challenges with the new types of mobility, but also brought a huge opportunity to accelerate the connectivity and informatization of transportation systems, particularly when we consider all the new forms of data that is becoming available. The primary challenge is how to take advantage of the enormous amount of data to discover knowledge, build effective models, and develop impactful applications. With the theoretical and experimental progress being made over the last two decades, data mining and machine learning technologies have become key approaches for parsing data, understanding information, and making informed decisions, especially as the rise of deep learning algorithms bringing new levels of performance to the analysis of large datasets. The combination of data mining and ITS can greatly benefit research and advances in shared mobility and CAVs.This dissertation focuses on knowledge discovery and data mining for shared mobility and CAV applications. When considering big data associated with shared mobility operations and CAV research, data mining techniques can be customized with transportation knowledge to initially parse the data. Then machine learning methods can be used to model the parsed data to elicit hidden knowledge. Finally, the discovered knowledge and extracted information can help in the development of effective shared mobility and CAV applications to achieve the goals of a safer, faster, and more eco-friendly transportation systems.In this dissertation, there are four main sections that are addressed. First, new methodologies are introduced for extracting lane-level road features from rough crowdsourced GPS trajectories via data mining, which is subsequently used as the fundamental information for CAV applications. The proposed method results in decimeter level accuracy, which satisfies the positioning needs for many macroscopic and microscopic shared mobility and CAV applications. Second, macroscopic ride-hailing service big data has been analyzed for demand prediction, vehicle operation, and system efficiency monitoring. The proposed deep learning algorithms increase the ride-hailing demand prediction accuracy to 80% and can help the fleet dispatching system reduce 30% of vacant travel distance. Third, microscopic automated vehicle perception data has been analyzed for a real-time computer vision system that can be used for lane change behavior detection. The proposed deep learning design combines the residual neural network image input with time serious control data and reaches 95% of lane change behavior prediction accuracy. Last but not least, new ride sharing and CAV applications have been simulated in a behavior modeling framework to analyze the impact of mobility and energy consumption, which addresses key barriers by quantifying the transportation system-wide mobility, energy and behavior impacts from new mobility technologies using real-world data

Deep reinforcement learning for multi-modal embodied navigation

Ce travail se concentre sur une tâche de micro-navigation en plein air où le but est de naviguer vers une adresse de rue spécifiée en utilisant plusieurs modalités (par exemple, images, texte de scène et GPS). La tâche de micro-navigation extérieure s’avère etre un défi important pour de nombreuses personnes malvoyantes, ce que nous démontrons à travers des entretiens et des études de marché, et nous limitons notre définition des problèmes à leurs besoins. Nous expérimentons d’abord avec un monde en grille partiellement observable (Grid-Street et Grid City) contenant des maisons, des numéros de rue et des régions navigables. Ensuite, nous introduisons le Environnement de Trottoir pour la Navigation Visuelle (ETNV), qui contient des images panoramiques avec des boîtes englobantes pour les numéros de maison, les portes et les panneaux de nom de rue, et des formulations pour plusieurs tâches de navigation. Dans SEVN, nous formons un modèle de politique pour fusionner des observations multimodales sous la forme d’images à résolution variable, de texte visible et de données GPS simulées afin de naviguer vers une porte d’objectif. Nous entraînons ce modèle en utilisant l’algorithme d’apprentissage par renforcement, Proximal Policy Optimization (PPO). Nous espérons que cette thèse fournira une base pour d’autres recherches sur la création d’agents pouvant aider les membres de la communauté des gens malvoyantes à naviguer le monde.This work focuses on an Outdoor Micro-Navigation (OMN) task in which the goal is to navigate to a specified street address using multiple modalities including images, scene-text, and GPS. This task is a significant challenge to many Blind and Visually Impaired (BVI) people, which we demonstrate through interviews and market research. To investigate the feasibility of solving this task with Deep Reinforcement Learning (DRL), we first introduce two partially observable grid-worlds, Grid-Street and Grid City, containing houses, street numbers, and navigable regions. In these environments, we train an agent to find specific houses using local observations under a variety of training procedures. We parameterize our agent with a neural network and train using reinforcement learning methods. Next, we introduce the Sidewalk Environment for Visual Navigation (SEVN), which contains panoramic images with labels for house numbers, doors, and street name signs, and formulations for several navigation tasks. In SEVN, we train another neural network model using Proximal Policy Optimization (PPO) to fuse multi-modal observations in the form of variable resolution images, visible text, and simulated GPS data, and to use this representation to navigate to goal doors. Our best model used all available modalities and was able to navigate to over 100 goals with an 85% success rate. We found that models with access to only a subset of these modalities performed significantly worse, supporting the need for a multi-modal approach to the OMN task. We hope that this thesis provides a foundation for further research into the creation of agents to assist members of the BVI community to safely navigate

Traffic Control Recognition with AN Attention Mechanism Using Speed-Profile and Satellite Imagery Data

Traffic regulators at intersections act as an essential factor that influences traffic flow and, subsequently, the route choices of commuters. A digital map that provides up-to-date traffic control information is beneficial not only for facilitating the commuters’ trips, but also for energy-saving and environmental protection. In this paper, instead of using expensive surveying methods, we propose an automatic way based on a Conditional Variational Autoencoder (CVAE) to recognize traffic regulators, i. e., arm rules at intersections, by leveraging the GPS data collected from vehicles and the satellite imagery retrieved from digital maps, i. e., Google Maps. We apply a Long Short-Term Memory to extract the motion dynamics over a GPS sequence traversed through the intersection. Simultaneously, we build a Convolutional Neural Network (CNN) to extract the grid-based local imagery information associated with each step of the GPS positions. Moreover, a self-attention mechanism is adopted to extract the spatial and temporal features over both the GPS and grid sequences. The extracted temporal and spatial features are then combined for detecting the traffic arm rules. To analyze the performance of our method, we tested it on a GPS dataset collected by driving vehicles in Hannover, a medium-sized German city. Compared to a Random Forest model and an Encoder-Decoder model, our proposed model achieved better results with both accuracy and F1-score of 0.90 for the three-class (arm rules of uncontrolled, traffic light, and priority sign) task. We also carried out ablation studies to further investigate the effectiveness of the GPS input branch, the image input branch, and the self-attention mechanism in our model