3D point cloud processing has been a critical task due to the increasing demand of a variety of applications such as urban planning and management, as-built mapping of industrial sites, infrastructure monitoring, and road safety inspection. Point clouds are mainly acquired from two sources, laser scanning and optical imaging systems. However, the original point clouds usually do not provide explicit semantic information, and the collected data needs to undergo a sequence of processing steps to derive and extract the required information. Moreover, according to application requirements, the outcomes from the point cloud processing could be different. This dissertation presents two tiers of data processing. The first tier proposes an adaptive data processing framework to deal with multi-source and multi-platform point clouds. The second tier introduces two point clouds processing strategies targeting applications mainly from urban environments and transportation corridors.<div><br></div><div>For the first tier of data processing, the internal characteristics (e.g., noise level and local point density) of data should be considered first since point clouds might come from a variety of sources/platforms. The acquired point clouds may have a large number of points. Data processing (e.g., segmentation) of such large datasets is time-consuming. Hence, to attain high computational efficiency, this dissertation presents a down-sampling approach while considering the internal characteristics of data and maintaining the nature of the local surface. Moreover, point cloud segmentation is one of the essential steps in the initial data processing chain to derive the semantic information and model point clouds. Therefore, a multi-class simultaneous segmentation procedure is proposed to partition point cloud into planar, linear/cylindrical, and rough features. Since segmentation outcomes could suffer from some artifacts, a series of quality control procedures are introduced to evaluate and improve the quality of the results.<br></div><div><br></div><div>For the second tier of data processing, this dissertation focuses on two applications for high human activity areas, urban environments and transportation corridors. For urban environments, a new framework is introduced to generate digital building models with accurate right-angle, multi-orientation, and curved boundary from building hypotheses which are derived from the proposed segmentation approach. For transportation corridors, this dissertation presents an approach to derive accurate lane width estimates using point clouds acquired from a calibrated mobile mapping system. In summary, this dissertation provides two tiers of data processing. The first tier of data processing, adaptive down-sampling and segmentation, can be utilized for all kinds of point clouds. The second tier of data processing aims at digital building model generation and lane width estimation applications.<br></div>

Les applications pour véhicules autonomes et les systèmes d’aide avancée à la conduite (Advanced Driving Assistance Systems - ADAS) mettent en oeuvre des processus permettant à des systèmes haut niveau de réaliser une prise de décision. Pour de tels systèmes, la connaissance du positionnement précis (ou localisation) du véhicule dans son environnement est un pré-requis nécessaire. Cette thèse s’intéresse à la détection de la structure de scène, au processus de localisation ainsi qu’à la modélisation d’erreurs. A partir d’un large spectre fonctionnel de systèmes de vision, de l’accessibilité d’un système de cartographie ouvert (Open Geographical Information Systems - GIS) et de la large diffusion des systèmes de positionnement dans les véhicules (Global Positioning System - GPS), cette thèse étudie la performance et la fiabilité d’une méthode de localisation utilisant ces différentes sources. La détection de marquage sur la route réalisée par caméra monoculaire est le point de départ permettant de connaître la structure de la scène. En utilisant, une détection multi-noyau avec pondération hiérarchique, la méthode paramétrique proposée effectue la détection et le suivi des marquages sur la voie du véhicule en temps réel. La confiance en cette source d’information a été quantifiée par un indicateur de vraisemblance. Nous proposons ensuite un système de localisation qui fusionne des informations de positionnement (GPS), la carte (GIS) et les marquages détectés précédemment dans un cadre probabiliste basé sur un filtre particulaire. Pour ce faire, nous proposons d’utiliser les marquages détectés non seulement dans l’étape de mise en correspondance des cartes mais aussi dans la modélisation de la trajectoire attendue du véhicule. La fiabilité du système de localisation, en présence d’erreurs inhabituelles dans les différentes sources d’information, est améliorée par la prise en compte de différents indicateurs de confiance. Ce mécanisme est par la suite utilisé pour identifier les sources d’erreur. Cette thèse se conclut par une validation expérimentale des méthodes proposées dans des situations réelles de conduite. Leurs performances ont été quantifiées en utilisant un véhicule expérimental et des données en libre accès sur internet. / Autonomous Vehicles (AV) applications and Advanced Driving Assistance Systems (ADAS) relay in scene understanding processes allowing high level systems to carry out decision marking. For such systems, the localization of a vehicle evolving in a structured dynamic environment constitutes a complex problem of crucial importance. Our research addresses scene structure detection, localization and error modeling. Taking into account the large functional spectrum of vision systems, the accessibility of Open Geographical Information Systems (GIS) and the widely presence of Global Positioning Systems (GPS) onboard vehicles, we study the performance and the reliability of a vehicle localization method combining such information sources. Monocular vision–based lane marking detection provides key information about the scene structure. Using an enhanced multi-kernel framework with hierarchical weights, the proposed parametric method performs, in real time, the detection and tracking of the ego-lane marking. A self-assessment indicator quantifies the confidence of this information source. We conduct our investigations in a localization system which tightly couples GPS, GIS and lane makings in the probabilistic framework of Particle Filter (PF). To this end, it is proposed the use of lane markings not only during the map-matching process but also to model the expected ego-vehicle motion. The reliability of the localization system, in presence of unusual errors from the different information sources, is enhanced by taking into account different confidence indicators. Such a mechanism is later employed to identify error sources. This research concludes with an experimental validation in real driving situations of the proposed methods. They were tested and its performance was quantified using an experimental vehicle and publicly available datasets.

Orientador: Igor Leite Freire / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Matemática Aplicada, 2013

Esta dissertação de mestrado apresenta um estudo do processo de formação de pelotões em rodovias de pista simples brasileiras e a dispersão desses pelotões nas faixas adicionais de subida. Este estudo foi realizado usando-se dados empíricos coletados na SP 255, uma rodovia de pista simples do estado de São Paulo. Um dos trechos estudados possuía faixas adicionais nos aclives e o outro era sem faixas adicionais. Para caracterizar a formação de pelotões, três aspectos foram estudados: a porcentagem de veículos em pelotões, o tamanho médio dos pelotões e a recomposição dos pelotões após o término de uma faixa adicional. Modelos matemáticos foram adaptados para representar o processo de formação de pelotões em rodovias brasileiras. Visando fornecer subsídios para futuros projetos de faixas adicionais, a dispersão dos pelotões nas faixas adicionais de subida foi analisada considerando-se as seguintes medidas de desempenho: taxa de ultrapassagem, velocidade média da corrente, porcentagem de veículos em pelotões e comprimento de faixa para a dispersão dos pelotões. Os resultados do estudo da dispersão dos pelotões mostram que as faixas adicionais melhoram o nível de serviço não apenas na direção onde são implantadas, mas também na direção do tráfego oposto uma vez que cerca de 70 a 75% do fluxo no sentido analisado divergem para as faixas adicionais, proporcionando uma porcentagem maior de gaps adequados para a dispersão dos pelotões do fluxo oposto. / This master thesis presents a study of the platoon formation process in the Brazilian two-lane roads and the dispersion of these platoons in the passing lanes. This study was developed using empirical data collected in the SP 255, a two-lane road of the state of São Paulo. One of the segments studied had passing lanes on upgrades and another was without passing lanes. In order to characterize the platoon formation, three aspects were studied: the percent of vehicle in platoon, the mean platoon size and the platoon recomposition after the end of a passing lane. Mathematical models were adapted to represent the platoon formation in the Brazilian roads. Aiming to aid future auxiliary lanes projects, the platoon dispersion in the climbing lanes was analyzed considering the following service measures: overtaking rate, mean speed, percent of vehicle traveling in platoons and lane length used to disperse the platoons. The results of the platoons dispersion study show that the auxiliary lanes improve the level of service not only in the direction of they are constructed, but also in the opposing flow. It\'s verified that around 70 to 75% of the flow in the analyzed direction diverge to auxiliary lanes, providing a greater percentage of gaps suitable to platoon dispersion in the opposing flow.

Rapid progress is being made towards the realization of autonomous cars. Since the technology is in its early stages, human intervention is still necessary in order to ensure hazard-free operation of autonomous driving systems. Substantial research efforts are underway to enhance driver and passenger safety in autonomous cars. Toward that end GreedyHaarSpiker, a real-time vision-based lane detection algorithm is proposed for road lane detection in different weather conditions. The algorithm has been implemented in Python 2.7 with OpenCV 3.0 and tested on a Raspberry Pi 3 Model B ARMv8 1GB RAM coupled to a Raspberry Pi camera board v2. To test the algorithm’s performance, the Raspberry Pi and the camera board were mounted inside a Jeep Wrangler. The algorithm performed better in sunny weather with no snow on the road. The algorithm’s performance deteriorated at night time or when the road surface was covered with snow.

Due to the degree of complexity related to measuring the advantage of establishing Electronic Toll Collection (ETC) systems, literature generally stops short of modeling an all-inclusive set of benefits of the system. In this research, a model that incorporates the impact on both the users and the society as a whole and evaluates the financial benefits over the lifespan of the ETC investment is developed. Most of the values for the parameters used for calculating the benefits are taken from Federal Highway Administration (FHWA) and from similar studies conducted by transportation agencies, which is the setting that has motivated the current research. These parameters are national averages and not region specific. The model will serve as a decision making tool to determine the number of ETC lanes over the manual and automatic lanes. The model has been used for toll plazas with different number of lanes to study the financial value of the benefits due to the ETC deployment. It is also used to study the effect of the traffic flow on the total benefits and recommendation has been made with respect to the time for the ETC deployment.

Major safety concerns are present at rural high speed intersections. When long uninterrupted tangents are located near rural intersections, the drivers can become inattentive to upcoming decision points. Traffic control devices could aid in mitigating these occurrences by warning drivers of upcoming decision points. One such device is transverse rumble strips, which act to provide motorists with an audible and tactile warning that their vehicle is approaching a decision point of critical importance to safety. The objective of this research was to determine if the presence of transverse rumble strips were an effective warning device for drivers approaching rural stop-controlled intersections. To evaluate the effectiveness of transverse rumble strips, vehicle speeds were measured at three locations along the approach to an intersection both before and after the installation of rumble strips. Vehicle speeds were measured at nine rural stop-controlled intersection sites in Texas. Overall, the installation of rumble strips generally produced small, but statistically significant (p ? 0.05), reductions in traffic speeds. There were some negative driver behavioral impacts (i.e., speed increases) that occurred after the installation of rumble strips. There were a few instances where speed change reductions of greater than 1 mph occurred, however, the overall trend was that speed change reductions were equal to or less than 1 mph. Although the rumble strips did not produce meaningful reductions in traffic speeds, they should still be considered based upon previous accident reductions and minimal installation costs.

This thesis is concerned with automotive active safety, and a central theme is a new safety function called Emergency Lane Assist (ELA). Automotive safety is often categorised into passive and active safety, where passive safety is concerned with reducing the effects of accidents and active safety aims at avoiding them. ELA detects lane departure manoeuvres that are likely to result in a collision and prevents them by applying a steering wheel torque. The ELA concept is based on traffic accident statistics, i.e., it is designed to give maximum safety based on information about real life traffic accidents. The ELA function puts tough requirements on the accuracy of the information from the sensors, in particular the road shape and the position of surrounding objects, and on robust threat assessment. Several signal processing methods have been developed and evaluated in order to improve the accuracy of the sensor information, and these improvements are also analysed in how they relate to the ELA requirements. Different threat assessment methods are also studied, and a common element in both the signal processing and the threat assessment is that they are based on driver behaviour models, i.e., they utilise the fact that depending on the traffic situation, drivers are more likely to behave in certain ways than others. Most of the methods are general and can be, and hopefully also will be, applied also in other safety systems, in particular when a complete picture of the vehicle surroundings is considered, including information about road and lane shape together with the position of vehicles and infrastructure. All methods in the thesis have been evaluated on authentic sensor data from actual and relevant traffic environments.

The purpose of this thsis is to develop a vision perception based Intelligent Vehicle Driving Assistant System ( IVDAS ), which utilizes CCD camera to capture the movement of vehicle and road image on DSP-Based . According to daytime and night time, we analyzed the full information in the image to acquire the important and proper characteristics about lane mark and vehicle. There are two sub-systems in our system , including Lane Mark Detection and Vehicle Detection. The main goal is to identify if there are existing vehicles in the front of or near our vehicle. This system can provide information for the Intelligent Vehicle to make decision to avoid accident happening and assisted driver in driving safely.

Major safety concerns are present at rural high speed intersections. When long uninterrupted tangents are located near rural intersections, the drivers can become inattentive to upcoming decision points. Traffic control devices could aid in mitigating these occurrences by warning drivers of upcoming decision points. One such device is transverse rumble strips, which act to provide motorists with an audible and tactile warning that their vehicle is approaching a decision point of critical importance to safety. The objective of this research was to determine if the presence of transverse rumble strips were an effective warning device for drivers approaching rural stop-controlled intersections. To evaluate the effectiveness of transverse rumble strips, vehicle speeds were measured at three locations along the approach to an intersection both before and after the installation of rumble strips. Vehicle speeds were measured at nine rural stop-controlled intersection sites in Texas. Overall, the installation of rumble strips generally produced small, but statistically significant (p ? 0.05), reductions in traffic speeds. There were some negative driver behavioral impacts (i.e., speed increases) that occurred after the installation of rumble strips. There were a few instances where speed change reductions of greater than 1 mph occurred, however, the overall trend was that speed change reductions were equal to or less than 1 mph. Although the rumble strips did not produce meaningful reductions in traffic speeds, they should still be considered based upon previous accident reductions and minimal installation costs.