This degree project investigates social and technological aspects of human-vehicle interactions with regards to driverless autonomous utilitarian vehicles in urban context and proposes the use of LED lighting as an external Human Machine Interface for vehicle pedestrian signaling.

Autonomous Vehicles (AVs) should result in tremendous benefits to safe human transportation. Recent reports indicate a global average of 3,287 road crash related fatalities a day with the blame, in most cases, assigned to the human driver. By replacing the main cause, AVs are predicted to significantly reduce road accidents -- some claiming up to a 90% reduction on US roads. However, achieving these numbers is not simple. AVs are expected to assume tasks that human drivers perform both consciously and unconsciously -- in some instances, with Machine Learning. AVs incur new levels of complexity that, if handled incorrectly, can result in failures that cause loss of human life and damage to the environment. Accidents involving SAE Level 2 vehicles have highlighted such failures and demonstrated that AVs have a long way to go. The path towards safe AVs includes system architectures that provide effective failure monitoring, detection and mitigation. These architectures must produce AVs that degrade gracefully and remain sufficiently operational in the presence of failures. We introduce Safe-AV, a fault tolerant safety architecture for AVs that is based on the commonly adopted E-Gas 3 Level Monitoring Concept, the Simplex Architecture and guided by a thorough hazard analysis in the form of Systems-Theoretic Process Analysis (STPA). We commenced the architecture design with a review of some modern AV accidents which helped identify the types of failures AVs can present and acted as a first step to our STPA. The hazard analysis was applied to an initial AV architecture (without safety mechanisms) consisting of components that should be present in a typical AV (based on the literature and our ideas). Our STPA identified the system level accidents, hazards and corresponding loss scenarios that led to well-founded safety requirements which, in turn, evolved the initial architecture into Safe-AV. / Thesis / Master of Applied Science (MASc)

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / In this paper, a devising scheme of Autonomous Vehicle Location and Navigation System is introduced firstly. Then, several key technologies used in the devising scheme are presented, which includes a data fusion method based on extended decentralized kalman filter technology, a map-matching method used to compensate the positioning error, and a digital map data processing method used to realize route planning algorithm. By this time, a sample machine based on the devising scheme introduced in this paper has already been worked out successfully. The availability and the advantages of these technologies have been demonstrated.

With the number of deaths, commute time, and injuries constantly rising due to human driving errors, it’s time for a new transportation system, where humans are no longer involved in driving decisions and vehicles are the only machine that decide the actions of a vehicle. To accomplish a fully autonomous world, it’s important for vehicles to be able to communicate instantly and report their movements in order to reduce accidents. This paper discusses four approaches to vehicle to vehicle communication, as well as the underlying standards and technology that enable vehicles to accomplish communicating.

<p>In the context of highway safety factors, road geometrics and pavement condition are of particular interest to highway managers as they fall within their direct control and therefore can be addressed through highway projects. In spite of the preponderance of econometric modeling in highway safety research, there still remain areas worthy of further investigation. These include 1) the lack of sufficient feedback to roadway preservation engineers regarding the impacts of road-surface condition on safety; 2) the inadequate feedback to roadway designers on optimal lane and shoulder width allocation; 3) the need for higher predictive capability and reliability of models that analyze roadway operations; and 4) the lack of realistic simulations to facilitate reliable safety impact studies regarding autonomous vehicles (AV). In an attempt to contribute to the existing knowledge in this domain and to throw more light on these issues, this dissertation proposes a novel framework for enhanced prediction of highway safety that incorporates machine learning and econometrics with optimization to evaluate and quantify the impacts of safety factors. In the traditional highway operating environment, the proposed framework is expected to help agencies improve their safety analysis. Using an Indiana crash dataset, this dissertation implements the framework, thereby 1) estimating the safety impacts of the road-surface condition with advanced econometric specifications, 2) optimizing space resource allocations across highway cross-sectional elements, and 3) predicting the fatality status of highway segments using machine learning algorithms. In addition, this dissertation discusses the opportunities and the expected safety impacts and benefits of AV in the emerging operating environment. The dissertation also presents a proposed deep learning-based autonomous driving simulation framework that addresses the limitations of AV testing and evaluation on in-service roads and test tracks.</p>

Les constructeurs automobiles fabriquant des systèmes de conduite automatisée ont besoin d’aborder les conséquences que ces systèmes peuvent avoir sur la sécurité routière. Notamment pour l’évaluation des gains de sécurité, la sécurisation des essais et l’analyse des accidents impliquant le véhicule autonome. Cependant, le cadre conceptuel actuel utilisé dans la sécurité routière peut ne pas être adapté pour l’analyse des changements et des nouvelles interactions introduits par l’automatisation du véhicule à travers toutes les échelles du système sociotechnique de transport routier.Le but de la thèse est d’appliquer une approche systémique fondée sur STAMP afin d'étudier les gains attendus du véhicule autonome en termes de sécurité routière, sécuriser les expérimentations et analyser les accidents impliquant ce type de véhicule, à travers toutes les échelles du système sociotechnique de transport routier.Afin de contribuer au calcul des gains du véhicule autonome sur la sécurité routière, la population cible d’un « highway pilot system» a été définie et des questions issue d’une analyse STPA (analyse des risques issue de STAMP) aidant à l’évaluation de l’efficacité du système ont été élaborées.Un cadre de sécurisation des expérimentations couvrant tous les niveaux du système a été mis en place au moyen d’une analyse STPA à deux échelles.Enfin, une méthode d’analyse des accidents impliquant un conducteur automatisé a été créé en intégrant des éléments issus de méthodes d’analyses des accidents de la route existantes et des éléments explicatifs développés spécialement à la méthode CAST (méthode d’analyse des accidents fondée sur STAMP). L’accident impliquant une Tesla en mai 2016 est le cas d’étude de cette nouvelle méthode, CASCAD.En conclusion, ces trois applications ont montré tout le potentiel d’une approche systémique fondée sur STAMP pour offrir un cadre conceptuel adapté à l’évaluation des conséquences sur la sécurité routière de la conduite automatisée. / As automakers develop automated driving systems, they must address the implications of such systems on road safety. Notably for the safety benefit assessment, trial safety and accident analysis. However, the existing conceptual framework in road safety may not be adapted to analyze the changes and new interactions introduced by vehicle automation at all the levels of the road transport sociotechnical system.The main objective of this thesis is to apply a systems theoretic approach based on STAMP to examine the safety benefit assessment, trial safety and accident analysis of automated driving across all the levels of the road transport sociotechnical system.This research first contributes to safety benefit assessment by estimating the target population of a highway pilot system and by generating questions derived from an STPA analysis (hazard analysis based on STAMP) to facilitate the evaluation of the influence of the highway pilot system on road safety.Next, this work establishes a framework to ensure trial safety across the macroscopic and microscopic levels of the vehicle trial system by structuring the outputs of two STPA analyses.Finally, this thesis integrates elements from existing crash analysis methods and newly developed guidance elements into CAST (an accident analysis method based on STAMP) to develop a new method for the accident analysis of crashes involving automated driving called CASCAD. The application of CASCAD was illustrated using the available information of the Tesla crash on May 2016.The three applications of this research show the potential of a STAMP-based approach to provide a suitable conceptual framework for the analysis of the implications of road safety on automated driving.

A proposta deste trabalho é contribuir com a construção de uma plataforma de veículo autônomo para viabilizar as pesquisas na área pelo Departamento de Engenharia de Transportes da USP. Até o momento o departamento dispõe de uma plataforma que, a partir de sua posição conhecida, consegue navegar autonomamente até um ponto de destino utilizando apenas uma solução GNSS, no caso, GPS. Para ampliar a mobilidade da plataforma, está sendo sugerida aqui, a adição de sensores inerciais ao veículo, para que ele consiga obter uma solução de posição mesmo em áreas sem cobertura GNSS. Um Sistema de Navegação Inercial não depende de infraestrutura externa, exceto para inicializar suas variáveis, o que neste caso pode ser feito com auxílio de um receptor GPS. Sensores inerciais de alto desempenho são caros, tem alta complexidade mecânica e em geral são de grande porte. A alternativa é o uso de sensores do tipo MEMS que são pequenos, fáceis de serem manipulados e apresentam baixo consumo de energia. A contrapartida é que a solução é mais susceptível a ruído do que seus pares que custam na faixa de centena de milhões de dólares. / The proposal of this paper is to build an autonomous vehicle platform to enable the researches in this area by the Transport Engineering Department of the USP. Until now the Department has a platform that, once its initial position is known, it can navigate autonomously to a destination point using only the GNSS, in this case, GPS. To expand the mobility resources of the platform, it is being suggested here the addition of inertial sensors to the vehicle, enabling it to acquire a position solution even in areas where there is no coverage of the GNSS. An Inertial Navigation System does not depend on an external infra-structure, with the exception on the initial setup, where the GPS can be used to provide this kind of initialization. High performance inertial sensors are expensive, have high mechanical complexity and in general are big. The alternative is the usage of MEMS sensors, which are small, easy to handle and has low power consumption. In the opposite side this solution is more susceptible to noises in comparison to those High performance sensors that cost hundreds of thousands of dollars.

O projeto de mestrado, denominado de forma abreviada como GPUServices, se insere no contexto da pesquisa e do desenvolvimento de métodos de processamento de dados de sensores tridimensionais aplicados a robótica móvel. Tais métodos serão chamados de serviços neste projeto e incluem algoritmos de pré-processamento de nuvens de pontos 3D com segmentação dos dados, a separação e identificação de zonas planares (chão, vias), e detecção de elementos de interesse (bordas, obstáculos). Devido à grande quantidade de dados a serem tratados em um curto espaço de tempo, esses serviços utilizam processamento paralelo por GPU para realizar o processamento parcial ou completo destes dados. A área de aplicação em foco neste projeto visa prover serviços para um sistema ADAS: veículos autônomos e inteligentes, forçando-os a se aproximarem de um sistema de processamento em tempo real devido ao contexto de direção autônoma. Os serviços são divididos em etapas de acordo com a metodologia do projeto, mas sempre buscando a aceleração com o uso de paralelismo inerente: O pré-projeto consiste de organizar um ambiente que seja capaz de coordenar todas as tecnologias utilizadas e que explore o paralelismo; O primeiro serviço tem a responsabilidade de extrair inteligentemente os dados do sensor que foi usado pelo projeto (Sensor laser Velodyne de múltiplos feixes), que se mostra necessário devido à diversos erros de leitura e ao formato de recebimento, fornecendo os dados em uma estrutura matricial; O segundo serviço em cooperação com o anterior corrige a desestabilidade espacial do sensor devido à base de fixação não estar perfeitamente paralela ao chão e devido aos amortecimentos do veículo; O terceiro serviço separa as zonas semânticas do ambiente, como plano do chão, regiões abaixo e acima do chão; O quarto serviço, similar ao anterior, realiza uma pré-segmentação das guias da rua; O quinto serviço realiza uma segmentação de objetos do ambiente, separando-os em blobs; E o sexto serviço utiliza de todos os anteriores para a detecção e segmentação das guias da rua. Os dados recebidos pelo sensor são na forma de uma nuvem de pontos 3D com grande potencial de exploração do paralelismo baseado na localidade das informações. Porém, sua grande dificuldade é a grande taxa de dados recebidos do sensor (em torno de 700.000 pontos/seg.), sendo esta a motivação deste projeto: usar todo o potencial do sensor de forma eficiente ao usar o paralelismo de programação GPU, disponibilizando assim ao usuário serviços de tratamento destes dados. / The master\'s project, abbreviated hence forth as GPUServices, fits in the context of research and development of three-dimensional sensor data processing methods applied to mobile robotics. Such methods will be called services in this project, which include a 3D point cloud preprocessing algorithms with data segmentation, separation and identification of planar areas (ground track), and also detecting elements of interest (borders, barriers). Due to the large amount of data to be processed in a short time, these services should use parallel processing, using the GPU to perform partial or complete processing of these data. The application area in focus in this project aims to provide services for an ADAS system: autonomous and intelligent vehicles, forcing them to get close to a real-time processing system due to the autonomous direction of context.The services are divided into stages according to the project methodology, but always striving for acceleration using inherent parallelism: The pre-project consists of organizing an environment for development that is able to coordinate all used technologies, to exploit parallelism and to be integrated to the system already used by the autonomous car; The first service has a responsibility to intelligently extract sensor data that will be used by the project (Laser sensor Velodyne multi-beam), it appears necessary because of the many reading errors and the receiving data format, hence providing data in a matrix structure; The second service, in cooperation with the above, corrects the spatial destabilization due to the sensor fixing base not perfectly parallel to the ground and due to the damping of the vehicle; The third service separates the environment into semantics areas such as ground plane and regions below and above the ground; The fourth service, similar to the above, performs a pre-segmentation of street cruds; The fifth service performs an environmental objects segmentation, separating them into blobs; The sixth service uses all prior to detection and segmentation of street guides.The received sensor data is structured in the form of a cloud of points. They allow processing with great potential for exploitation of parallelism based on the location of the information. However, its major difficulty is the high rate of data received from the sensor (around 700,000 points/sec), and this gives the motivation of this project: to use the full potential of sensor to efficiently use the parallelism of GPU programming, therefore providing data processing services to the user, providing services that helps and make the implementation of ADAS systems easier and/or faster.

abstract: Laser radars or lidar’s have been used extensively to remotely study winds within the atmospheric boundary layer and atmospheric transport. Lidar sensors have become an important tool within the meteorology and the wind energy community. For example, Doppler lidars are used frequently in wind resource assessment, wind turbine control as well as in atmospheric science research. A Time of Flight based (ToF) direct detection lidar sensor is used in vehicles to navigate through complex and dynamic environments autonomously. These optical sensors are used to map the environment around the car accurately for perception and localization tasks that help achieve complete autonomy. This thesis begins with a detailed discussion on the fundamentals of a Doppler lidar system. The laser signal flow path to and from the target, the optics of the system and the core signal processing algorithms used to extract velocity information, were studied to get closer to the hardware of a Doppler lidar sensor. A Doppler lidar simulator was built to study the existing signal processing algorithms to detect and estimate doppler frequency, and radial velocity information. Understanding the sensor and its processing at the hardware level is necessary to develop new algorithms to detect and track specific flow structures in the atmosphere. For example, the aircraft vortices have been a topic of extensive research and doppler lidars have proved to be a valuable sensor to detect and track these coherent flow structures. Using the lidar simulator a physics based doppler lidar vortex algorithm is tested on simulated data to track a pair of counter rotating aircraft vortices. At a system level the major components of a time of flight lidar is very similar to a Doppler lidar. The fundamental physics of operation is however different. While doppler lidars are used for radial velocity measurement, ToF sensors as the name suggests provides precise depth measurements by measuring time of flight between the transmitted and the received pulses. The second part of this dissertation begins to explore the details of ToF lidar system. A system level design, to build a ToF direct detection lidar system is presented. Different lidar sensor modalities that are currently used with sensors in the market today for automotive applications were evaluated and a 2D MEMS based scanning lidar system was designed using off-the shelf components. Finally, a range of experiments and tests were completed to evaluate the performance of each sub-component of the lidar sensor prototype. A major portion of the testing was done to align the optics of the system and to ensure maximum field of view overlap for the bi-static laser sensor. As a laser range finder, the system demonstrated capabilities to detect hard targets as far as 32 meters. Time to digital converter (TDC) and an analog to digital converter (ADC) was used for providing accurate timing solutions for the lidar prototype. A Matlab lidar model was built and used to perform trade-off studies that helped choosing components to suit the sensor design specifications. The size, weight and cost of these lidar sensors are still very high and thus making it harder for automotive manufacturers to integrate these sensors into their vehicles. Ongoing research in this field is determined to find a solution that guarantees very high performance in real time and lower its cost over the next decade as components get cheaper and can be seamlessly integrated with cars to improve on-road safety. / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2018

L'augmentation continue du nombre de véhicules a sans cesse conduit à innover pour mieux réguler le trafic routier. Aujourd'hui, l'automatisation des véhicules et la communication inter-véhiculaire rend possible une nouvelle approche : l'intersection autonome et coopérative (IAC) dans laquelle les véhicules autonomes communiquent pour négocier leur droit de passage à l'intersection. Théoriquement plus efficace, plus sûre et plus économique que les approches traditionnelles, son implémentation et son déploiement constituent néanmoins un défi scientifique et technologique. Dans ce mémoire, nous proposons plusieurs contributions soutenant ce développement : une stratégie de contrôle longitudinal et latéral du véhicule conçues pour fonctionner avec des informations discrètes et présentant une latence (provenant de la communication sans-fil ou du positionnement satellite), une solution de prévention des risques d'inter-blocage spécifique à l'IAC, et enfin, l'implémentation et l'expérimentation d'un prototype réel d'IAC avec trois véhicules autonomes. Un travail exploratoire est ensuite fourni pour généraliser les principes de l'IAC, afin d'automatiser et optimiser les opérations de changement de voie et d'insertion. / The continuous increase in the number of vehicles has constantly led to innovation to improve the regulation of road traffic. Today, vehicle automation and inter-vehicular communication make possible a new approach: the autonomous and cooperative intersection (ACI) in which autonomous vehicles communicate to negotiate their right of way at the intersection. Theoretically more effective, safer and more economical than traditional approaches, its implementation and deployment constitute a scientific and technological challenge. In this paper, we propose several contributions supporting this development: a longitudinal and lateral vehicle control strategy designed to work with information discrete and suffering from latency (coming from wireless communication or satellite positioning), a preventive solution of the risks of deadlock specific to the ACI, and finally the implementation and testing of a real prototype of ACI with three autonomous vehicles. An exploratory work is then provided to generalize the principles of the ACI, in order to automate and optimize the operations of lane change and insertion.