Classification of road users detected and tracked with LiDAR at intersections

Data collection is a necessary component of transportation engineering. Manual data collection methods have proven to be inefficient and limited in terms of the data required for comprehensive traffic and safety studies. Automatic methods are being introduced to characterize the transportation system more accurately and are providing more information to better understand the dynamics between road users. Video data collection is an inexpensive and widely used automated method, but the accuracy of video-based algorithms is known to be affected by obstacles and shadows and the third dimension is lost with video camera data collection. The impressive progress in sensing technologies has encouraged development of new methods for measuring the movements of road users. The Center for Road Safety at Purdue University proposed application of a LiDAR-based algorithm for tracking vehicles at intersections from a roadside location. LiDAR provides a three-dimensional characterization of the sensed environment for better detection and tracking results. The feasibility of this system was analyzed in this thesis using an evaluation methodology to determine the accuracy of the algorithm when tracking vehicles at intersections. According to the implemented method, the LiDAR-based system provides successful detection and tracking of vehicles, and its accuracy is comparable to the results provided by frame-by-frame extraction of trajectory data using video images by human observers. After supporting the suitability of the system for tracking, the second component of this thesis focused on proposing a classification methodology to discriminate between vehicles, pedestrians, and two-wheelers. Four different methodologies were applied to identify the best method for implementation. The KNN algorithm, which is capable of creating adaptive decision boundaries based on the characteristics of similar observations, provided better performance when evaluating new locations. The multinomial logit model did not allow the inclusion of collinear variables into the model. Overfitting of the training data was indicated in the classification tree and boosting methodologies and produced lower performance when the models were applied to the test data. Despite ANOVA analysis not supporting superior performance by a competitor, the objective of classifying movements at intersections under diverse conditions was achieved with the KNN algorithm and was chosen as the method to implement with the existing algorithm

Exploring AI Moral Reasoning

Artificial intelligence is being deployed in increasingly autonomous systems where it will have to make moral decisions. However, the rapid growth in artificial intelligence is outpacing the research in building explainable systems. In this paper, a number of problems around one facet of explainable artificial intelligence, training data, is explored. Possible solutions to these problems are presented. Additionally, the human decision-making process in unavoidable accident scenarios is explored through qualitative analysis of survey results

Detecting Risk Factors of Road Work Zone Crashes from the Information Provided in Police Crash Reports: The Case Study of Portugal

Several studies have shown that European police crash reports provide different detail degrees of work zone crash-related data. In this sense, the present study aims to verify the possibility of identifying significant risk factors involved in the occurrence of road work zone crashes with casualties, based on the official data usually available, through a descriptive, binary logistic, and probit regression statistical analysis. To accomplish the analysis, a total of 2597 police-reports related to 1767 Portuguese work zone crashes that occurred during the 2013–2015 period were considered and binary logistic and probit regression models were estimated by the main type of crash, contributing factor, and driver age group. Fifteen explanatory variables, selected based on the literature review and crash data provided in police crash reports, were considered in the analysis. The results obtained for the estimated coefficients and goodness-of-fit test values were found very similar for both link functions (logit and probit) and it was possible to identify risk factors. The modeling results pointed to excessive speed, disregard for vertical signs, luminosity, intersections, and motorcycle and heavy vehicle involvement as the most significant risk factors. Given the results, it is possible to conclude that binary logistic regression can be used in the statistical analysis of the available police official work zone crash data to identify and get some insight into the risk factors involved in work zone crashes. Data analysis also revealed the need to promote adequate and complete crash report filling by police officers. While police crash reports are not revised and standardized to incorporate more detailed work zone crash information, this approach can be used to support a more efficient road operation decision making and the review of some aspects related to work zone layout design.info:eu-repo/semantics/publishedVersio

On Minds' Localization

A confluence of clues from a range of academic topics suggests that minds localization in nature consists of relativistically moving microphysical particles, whose motion is physiologically modulated. Here those clues are shown to imply that the localization of the operations of observers (minds or existentialities) in nature are the actions carriers of a force field, which action carriers are slightly slowed from near-c speed motion by electroneurobiological variations in brain physiology – thus gating through relativistic time dilation the observer’s time resolution and putting her or him in operative connection or disconnection with the cerebral representation of the surrounding occurrences. In this scenario, minds as well as sensory knowledge acquire a precise definition and appear situated in a particular point of causal sequences. Summary in general terms: Why were minds selected to turn accidents into opportunities, i. e., to progress toward biological goals through appropriate steps for which the instructions are nonetheless undefinable? Minds appear situated in certain force-carrying particles whose speed sets wakefulness or sleep. Through this force, observable by its influence on the evolutionary process, minds and bodies interact. Physical actions impinging on a mind generate in it physical reactions whose causal efficiency gets exhausted, so that the reactions cannot continue their causal series. In exchange, they become sensorially known. On them the mind then takes efficient initiatives – whereby minds acquire intellectual development – generating changes. The broken causal sequence seems to be what enables minds for their biological role. Summary in technical terms: Observers’ localization in nature might be relativistically moving particles whose motion is physiologically modulated. Transdisciplinary clues imply that speed variation is imposed onto some action carriers of a force field by their coupling with intensity variations of an overlapping field. The operations of observers (minds or existentialities) in nature seem localized in such actions carriers, slightly slowed from near-c speed motion by electroneurobiological variations – which thus gate the observer’s time resolution and put her or him in operative connection or disconnection with the surroundings. Thereby minds and sensory knowledge appear in a particular point of causal sequences. ---------------- Keywords: Piaget causality mental causation evolution volition free-will pleasure/pain awareness self-consciousness evolution attention genetic epistemology gnoseology philosophical anthropology cerebral biophysics brain- mind relationships cadacualtez cadacualtic cilia ciliary cellular cognition electroneurodynamics engram epistemology memory mind-brain mind definition memoria nervous system evolution neural networks neurobiology cognitive neuroscience neuropsychiatry noergy nous-poietikos ontology consciousness paleontology person philosophy Precambrian psychopathology psychology psychism psychiatry recall special-relativity semovience sleep-biophysics shock soul time perception interval transform ultrahistory schizophrenia Turing machines vegetative artificial-lif

Toward Robust Sensing for Autonomous Vehicles: An Adversarial Perspective

Autonomous Vehicles rely on accurate and robust sensor observations for safety critical decision-making in a variety of conditions. Fundamental building blocks of such systems are sensors and classifiers that process ultrasound, RADAR, GPS, LiDAR and camera signals~\cite{Khan2018}. It is of primary importance that the resulting decisions are robust to perturbations, which can take the form of different types of nuisances and data transformations, and can even be adversarial perturbations (APs). Adversarial perturbations are purposefully crafted alterations of the environment or of the sensory measurements, with the objective of attacking and defeating the autonomous systems. A careful evaluation of the vulnerabilities of their sensing system(s) is necessary in order to build and deploy safer systems in the fast-evolving domain of AVs. To this end, we survey the emerging field of sensing in adversarial settings: after reviewing adversarial attacks on sensing modalities for autonomous systems, we discuss countermeasures and present future research directions

Head Detection and Tracking for an Intelligent Room

We present a novel feature extraction method, which employs a histogram of transition feature, as an input to a SVM classifier. This feature relies on foreground extraction. We also evaluate some foreground extraction method. To evaluate the performance of this feature, we use it for head detection. Then, by applying a combination of the Harris corner detector and Lucas-Kanade tracker and motion pattern, we track the head position. The performance of the proposed method is experimentally shown.SICE Annual Conference 2014 - International conference on Instrumentation, Control, Information Technology and System Integration, September 9-12, 2014, Hokkaido University, Sapporo, Japa

A data-driven approach to road accidents in the municipality of Lisbon

Traffic accidents in urban areas lead to reduced quality of life and social inequality in cities, specially in third world countries. The growth of the urban mesh and the population density is seldom accompanied by the development or sizing of the road infrastructure. It is a fact that the number and severity of road accidents in Portugal have been decreasing over the last thirty years, bringing us closer to the European average. However, despite these facts, the situation remains worrying. Despite the adoption of programs such as the European Commission Road Safety Program and the recent EU Road Safety Policy Framework 2021-2030 or, on a national basis, the PENSE 2020 - National Strategic Plan for Road Safety the number of road accidents with victims in the district of Lisbon is still higher than the European average. Thus, and for this dissertation, we conducted an exploratory data analysis (EDA) on the combined data of traffic incidents recorded in the occurrence management system of the Lisbon Fire Brigade Regiment (RSB) and the road accidents reported to ANSR by the security forces (GNR and PSP) through the Statistical Bulletin of Traffic Accidents (BEAV). Furthermore, with data from occurrences in the Municipality of Lisbon between 2010 and 2020, to identify the existence of Black Spots in Lisbon's roads and which are the most significant and contributing factors to explain their existence. The data on road accidents were also georeferenced to capitalize their spatial existence and, consequently, better understand the existing spatial patterns and risk factors. Subsequently, through the use of the ArcGIS Pro we apply the algorithms of the Kernel Density and Hot Spot Analysis (Getis-Ord Gi*) tools, identifying the existence of the black spots, and that human, environmental and circumstantial factors have an influence on the severity of accidents, being the content validity guaranteed through an expert committee. This way, our research goal is to contribute to identify accident concentration areas in the city of Lisbon (hotspots), considering their influencing conditions.Os acidentes de trânsito em áreas urbanas conduzem à redução da qualidade de vida e à desigualdade social nas cidades, especialmente nos países em desenvolvimento. O crescimento da malha urbana, assim como, a densidade populacional raramente é acompanhada pelo desenvolvimento ou dimensionamento da infraestrutura rodoviária. É um facto que o número e a gravidade dos acidentes rodoviários em Portugal têm vindo a diminuir ao longo dos últimos trinta anos, o que permitiu aproximarmos da média Europeia, apesar destes factos a situação continua a ser preocupante. Apesar da adoção de programas como o Programa de Segurança Rodoviária da Comissão Europeia ou, numa base nacional, o PENSE 2020 - Plano Estratégico Nacional para a Segurança Rodoviária os números de acidentes de viação com vítimas no distrito de Lisboa continuam a ser mais elevados do que a média europeia. Desta forma e para efeitos deste trabalho realizamos uma análise de dados exploratória (AED) aos dados dos incidentes de transito registados no sistema de gestão de ocorrências do Regimento de Sapadores Bombeiros de Lisboa e a os dados de acidentes rodoviários reportados à ANSR pelas forças de segurança (GNR e PSP) através do Boletim Estatístico de Acidentes de Viação (BEAV) e ocorridos no concelho de Lisboa entre 2010 e 2020 por forma a identificar a existência de Pontos Negros nas vias de Lisboa e quais os fatores mais significantes e contribuintes que permitam explicar a sua existência. Os dados relativos aos acidentes rodoviários foram também georreferenciados para capitalizar a sua existência espacial e, consequentemente, compreender melhor os padrões espaciais existentes e os fatores de risco. Posteriormente através do recurso ArcGIS Pro aplicaram-se os algoritmos das ferramentas Densidade de Kernel e Hot Spot Analysis (Getis-Ord Gi*), identificando a existência dos pontos negros, e que fatores humanos, ambientais e circunstanciais têm influência na gravidade dos acidentes e que algumas variáveis de exposição foram consideradas importantes na explicação da ocorrência dos mesmos, sendo a validade do conteúdo garantida através de uma comissão de especialistas. Pretende-se, assim, contribuir para a identificação das zonas de concentração de acidentes da cidade de Lisboa (hotspots), tendo em conta as suas condições influenciadoras. Potenciando a segurança rodoviária no município

Methodology and Algorithms for Pedestrian Network Construction

With the advanced capabilities of mobile devices and the success of car navigation systems, interest in pedestrian navigation systems is on the rise. A critical component of any navigation system is a map database which represents a network (e.g., road networks in car navigation systems) and supports key functionality such as map display, geocoding, and routing. Road networks, mainly due to the popularity of car navigation systems, are well defined and publicly available. However, in pedestrian navigation systems, as well as other applications including urban planning and physical activities studies, road networks do not adequately represent the paths that pedestrians usually travel. Currently, there are no techniques to automatically construct pedestrian networks, impeding research and development of applications requiring pedestrian data. This coupled with the increased demand for pedestrian networks is the prime motivation for this dissertation which is focused on development of a methodology and algorithms that can construct pedestrian networks automatically. A methodology, which involves three independent approaches, network buffering (using existing road networks), collaborative mapping (using GPS traces collected by volunteers), and image processing (using high-resolution satellite and laser imageries) was developed. Experiments were conducted to evaluate the pedestrian networks constructed by these approaches with a pedestrian network baseline as a ground truth. The results of the experiments indicate that these three approaches, while differing in complexity and outcome, are viable for automatically constructing pedestrian networks