Performance Boundary Identification for the Evaluation of Automated Vehicles using Gaussian Process Classification

Safety is an essential aspect in the facilitation of automated vehicle deployment. Current testing practices are not enough, and going beyond them leads to infeasible testing requirements, such as needing to drive billions of kilometres on public roads. Automated vehicles are exposed to an indefinite number of scenarios. Handling of the most challenging scenarios should be tested, which leads to the question of how such corner cases can be determined. We propose an approach to identify the performance boundary, where these corner cases are located, using Gaussian Process Classification. We also demonstrate the classification on an exemplary traffic jam approach scenario, showing that it is feasible and would lead to more efficient testing practices.Comment: 6 pages, 5 figures, accepted at 2019 IEEE Intelligent Transportation Systems Conference - ITSC 2019, Auckland, New Zealand, October 201

Towards a Smart World: Hazard Levels for Monitoring of Autonomous Vehicles’ Swarms

This work explores the creation of quantifiable indices to monitor the safe operations and movement of families of autonomous vehicles (AV) in restricted highway-like environments. Specifically, this work will explore the creation of ad-hoc rules for monitoring lateral and longitudinal movement of multiple AVs based on behavior that mimics swarm and flock movement (or particle swarm motion). This exploratory work is sponsored by the Emerging Leader Seed grant program of the Mineta Transportation Institute and aims at investigating feasibility of adaptation of particle swarm motion to control families of autonomous vehicles. Specifically, it explores how particle swarm approaches can be augmented by setting safety thresholds and fail-safe mechanisms to avoid collisions in off-nominal situations. This concept leverages the integration of the notion of hazard and danger levels (i.e., measures of the “closeness” to a given accident scenario, typically used in robotics) with the concept of safety distance and separation/collision avoidance for ground vehicles. A draft of implementation of four hazard level functions indicates that safety thresholds can be set up to autonomously trigger lateral and longitudinal motion control based on three main rules respectively based on speed, heading, and braking distance to steer the vehicle and maintain separation/avoid collisions in families of autonomous vehicles. The concepts here presented can be used to set up a high-level framework for developing artificial intelligence algorithms that can serve as back-up to standard machine learning approaches for control and steering of autonomous vehicles. Although there are no constraints on the concept’s implementation, it is expected that this work would be most relevant for highly-automated Level 4 and Level 5 vehicles, capable of communicating with each other and in the presence of a monitoring ground control center for the operations of the swarm

A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration

Development of Detection and Tracking Systems for Autonomous Vehicle using Machine Learning

A thesis presented to the faculty of the Elmer R. Smith College of Business and Technology at Morehead State University in partial fulfillment of the requirements for the Degree Master of Science by Tyler Ward on April 25, 2023

Video Stream Adaptation In Computer Vision Systems

Computer Vision (CV) has been deployed recently in a wide range of applications, including surveillance and automotive industries. According to a recent report, the market for CV technologies will grow to $33.3 billion by 2019. Surveillance and automotive industries share over 20% of this market. This dissertation considers the design of real-time CV systems with live video streaming, especially those over wireless and mobile networks. Such systems include video cameras/sensors and monitoring stations. The cameras should adapt their captured videos based on the events and/or available resources and time requirement. The monitoring station receives video streams from all cameras and run CV algorithms for decisions, warnings, control, and/or other actions. Real-time CV systems have constraints in power, computational, and communicational resources. Most video adaptation techniques considered the video distortion as the primary metric. In CV systems, however, the main objective is enhancing the event/object detection/recognition/tracking accuracy. The accuracy can essentially be thought of as the quality perceived by machines, as opposed to the human perceptual quality. High-Efficiency Video Coding (HEVC) is a recent encoding standard that seeks to address the limited communication bandwidth problem as a result of the popularity of High Definition (HD) videos. Unfortunately, HEVC adopts algorithms that greatly slow down the encoding process, and thus results in complications in real-time systems. This dissertation presents a method for adapting live video streams to limited and varying network bandwidth and energy resources. It analyzes and compares the rate-accuracy and rate-energy characteristics of various video streams adaptation techniques in CV systems. We model the video capturing, encoding, and transmission aspects and then provide an overall model of the power consumed by the video cameras and/or sensors. In addition to modeling the power consumption, we model the achieved bitrate of video encoding. We validate and analyze the power consumption models of each phase as well as the aggregate power consumption model through extensive experiments. The analysis includes examining individual parameters separately and examining the impacts of changing more than one parameter at a time. For HEVC, we develop an algorithm that predicts the size of the block without iterating through the exhaustive Rate Distortion Optimization (RDO) method. We demonstrate the effectiveness of the proposed algorithm in comparison with existing algorithms. The proposed algorithm achieves approximately 5 times the encoding speed of the RDO algorithm and 1.42 times the encoding speed of the fastest analyzed algorithm

Monitoring and Self-diagnosis of Civil Engineering Structures: Classical and Innovative Applications

Eventi estremi come esplosioni o terremoti possono avere un profondo impatto nella sicurezza degli edifici. Le zone sismiche devono convivere con questi tragici eventi, per questo monitorare in maniera continua le condizioni di salute di una struttura è necessario e auspicabile in molti casi. Il monitoraggio strutturale (Structural Health Monitoring – SHM) rappresenta un potente strumento per la valutazione del comportamento dinamico della struttura monitorata. Fino a pochi anni fa queste tecniche erano impiegate prevalentemente in ambito meccanico, aeronautico e nell’ingegneria aerospaziale. Al giorno d’oggi, la riduzione dei costi della strumentazione, sistemi di acquisizione dati di nuova generazione e l’incremento continuo dell’efficienta nelle analisi numeriche hanno reso possibile l’applicazione di queste tecniche anche a strutture civili ordinarie. Le tecniche di monitoraggio strutturale vengono applicate non solo in grandi infrastrutture come ponti, dighe o grattacieli, ma anche in strutture storiche o edifici residenziali. In questo contesto questa tesi tenta di esaminare differenti aspetti del monitoraggio strutturale, in particolar modo riferite a edifici ordinari. Attraverso tecniche Output-Only (Operational Modal Analysis – OMA) sono state monitorate diverse strutture civili con reti di sensori cablate, al fine di ottenere il comportamento dinamico strutturale nelle reali condizioni opertive. Particolare attenzione è stata focalizzata in un altra importante tematica dell’ingegneria strutturale: il danneggiamento strutturale. Attraverso un approccio numerico viene presentato un nuovo metodo per la localizzazione e quantificazione del danno a seguito di un evento sismico. In alternativa alla classica rete cablata, è stato sviluppato un sistema di acquisizione con sensori wireless (Wireless Sensor Network – WSN). I principali risultati ottenuti con questa applicazione vengono riportati nella presente tesi, unitamente al design dei sensori low-cost. Con l’ausilio della sensoristica sviluppata è stato monitorato un edificio storico in muratura, mostrando i risultati positivi ottenuti a seguito della campagna di acquisizione di rumore ambientale (Ambient Vibration Survey -AVS).Extreme events like explosions and earthquakes may have a deep impact on building safety. Seismic regions must live with these tragic events, so that continuous monitoring of structure health conditions is necessary in many cases. Structural Health Monitoring (SHM) represents a powerful tool for the evaluation of dynamic behavior of monitored structures. Until a few years ago these techniques were widely employed especially in mechanical, aeronautical and aerospace engineering. Nowadays, the reduction of equipment costs, the new generation of data acquisition systems, together with the continuous improvement of computational analysis have made it possible to apply SHM also to civil structures without strategic importance. SHM has moved from large infrastructures like bridges, dams and skyscrapers to historical heritage and residential buildings. In this background, the present work tries to examine different aspects of SHM applications, especially referred to ordinary buildings. Using Operational Modal Analysis (OMA) techniques, several civil structures have been monitored through a wired network sensor, in order to obtain the dynamic behavior in operating conditions. The relevant data collection provides a useful tool for calibrating the accuracy and sensitivity of similar SHM case studies. Specific attention is focused in another important issue in civil and in mechanical engineering: detection of structural damages. Through a numerical approach, a new method for damage localization and quantification is proposed. Besides the traditional wired acquisition system a Wireless Sensor Network (WSN) has been developed. The issues related to the usage of low-cost sensors and new generation data acquisition tools for non-destructive structural testing are discussed. Using the WSN an historical masonry building has been monitored, showing the positive results obtained following the Ambient Vibration Survey (AVS)