Automated highway systems : platoons of vehicles viewed as a multiagent system

Tableau d'honneur de la Faculté des études supérieures et postdoctorales, 2005-2006La conduite collaborative est un domaine lié aux systèmes de transport intelligents, qui utilise les communications pour guider de façon autonome des véhicules coopératifs sur une autoroute automatisée. Depuis les dernières années, différentes architectures de véhicules automatisés ont été proposées, mais la plupart d’entre elles n’ont pas, ou presque pas, attaqué le problème de communication inter véhicules. À l’intérieur de ce mémoire, nous nous attaquons au problème de la conduite collaborative en utilisant un peloton de voitures conduites par des agents logiciels plus ou moins autonomes, interagissant dans un même environnement multi-agents: une autoroute automatisée. Pour ce faire, nous proposons une architecture hiérarchique d’agents conducteurs de voitures, se basant sur trois couches (couche de guidance, couche de management et couche de contrôle du trafic). Cette architecture peut être utilisée pour développer un peloton centralisé, où un agent conducteur de tête coordonne les autres avec des règles strictes, et un peloton décentralisé, où le peloton est vu comme une équipe d’agents conducteurs ayant le même niveau d’autonomie et essayant de maintenir le peloton stable.Collaborative driving is a growing domain of Intelligent Transportation Systems (ITS) that makes use of communications to autonomously guide cooperative vehicles on an Automated Highway System (AHS). For the past decade, different architectures of automated vehicles have been proposed, but most of them did not or barely addressed the inter-vehicle communication problem. In this thesis, we address the collaborative driving problem by using a platoon of cars driven by more or less autonomous software agents interacting in a Multiagent System (MAS) environment: the automated highway. To achieve this, we propose a hierarchical driving agent architecture based on three layers (guidance layer, management layer and traffic control layer). This architecture can be used to develop centralized platoons, where the driving agent of the head vehicle coordinates other driving agents by applying strict rules, and decentralized platoons, where the platoon is considered as a team of driving agents with a similar degree of autonomy, trying to maintain a stable platoon

Structural Learning of Attack Vectors for Generating Mutated XSS Attacks

Web applications suffer from cross-site scripting (XSS) attacks that resulting from incomplete or incorrect input sanitization. Learning the structure of attack vectors could enrich the variety of manifestations in generated XSS attacks. In this study, we focus on generating more threatening XSS attacks for the state-of-the-art detection approaches that can find potential XSS vulnerabilities in Web applications, and propose a mechanism for structural learning of attack vectors with the aim of generating mutated XSS attacks in a fully automatic way. Mutated XSS attack generation depends on the analysis of attack vectors and the structural learning mechanism. For the kernel of the learning mechanism, we use a Hidden Markov model (HMM) as the structure of the attack vector model to capture the implicit manner of the attack vector, and this manner is benefited from the syntax meanings that are labeled by the proposed tokenizing mechanism. Bayes theorem is used to determine the number of hidden states in the model for generalizing the structure model. The paper has the contributions as following: (1) automatically learn the structure of attack vectors from practical data analysis to modeling a structure model of attack vectors, (2) mimic the manners and the elements of attack vectors to extend the ability of testing tool for identifying XSS vulnerabilities, (3) be helpful to verify the flaws of blacklist sanitization procedures of Web applications. We evaluated the proposed mechanism by Burp Intruder with a dataset collected from public XSS archives. The results show that mutated XSS attack generation can identify potential vulnerabilities.Comment: In Proceedings TAV-WEB 2010, arXiv:1009.330

Why computational models are better than verbal theories: the case of nonword repetition

Tests of nonword repetition (NWR) have often been used to examine children’s phonological knowledge and word learning abilities. However, theories of NWR primarily explain performance either in terms of phonological working memory or long-term knowledge, with little consideration of how these processes interact. One theoretical account that focuses specifically on the interaction between short-term and long-term memory is the chunking hypothesis. Chunking occurs because of repeated exposure to meaningful stimulus items, resulting in the items becoming grouped (or chunked); once chunked, the items can be represented in short-term memory using one chunk rather than one chunk per item. We tested several predictions of the chunking hypothesis by presenting 5-6 year-old children with three tests of NWR that were either high, medium, or low in wordlikeness. The results did not show strong support for the chunking hypothesis, suggesting that chunking fails to fully explain children’s NWR behavior. However, simulations using a computational implementation of chunking (namely CLASSIC, or Chunking Lexical And Sublexical Sequences In Children) show that, when the linguistic input to 5-6 year old children is estimated in a reasonable way, the children’s data is matched across all three NWR tests. These results have three implications for the field: (a) a chunking account can explain key NWR phenomena in 5-6 year old children; (b) tests of chunking accounts require a detailed specification both of the chunking mechanism itself and of the input on which the chunking mechanism operates; and (c) verbal theories emphasizing the role of long-term knowledge (such as chunking) are not precise enough to make detailed predictions about experimental data, but computational implementations of the theories can bridge the gap

Collaborative driving system using teamwork for platoon formations

Collaborative driving is a growing domain of Intelligent Transportation Systems (ITS) that makes use of communications to autonomously guide cooperative vehicles on an Automated Highway System (AHS). In this paper,weaddressthisissuebyusingaplatoonofcarsconsidered as more or less autonomous software agents. To do that, we propose a hierarchical architecture based on three layers (guidance layer, management layer and traffic control layer) which can be used to develop centralized platoons (where a head vehicle-agent coordinates other vehicle-agents by applying its coordination rule) and decentralized platoons (where the platoon is considered as a team of vehicle-agents trying to maintain the platoon). The latter decentralized model will mainly consider a teamwork related model using architectures like STEAM. These different coordination models will be compared using simulation scenarios to provide arguments for and against each approach. 1

Diesel engine exhaust exposures in two underground mines

Exposure to diesel engine exhaust (DE) is a major concern in underground mines. It has been linked to cardiopulmonary diseases and is classified as a human carcinogen. The goal of this study is to assess DE exposures in workers at two underground gold mines, to compare exposure levels within and between the mines, and to compare different methods of measuring DE exposures, namely respirable combustible dust (RCD), elemental carbon (EC) and total carbon (TC). Ambient and personal breathing zone (PBZ) measurements were taken. Side-by-side monitoring of RCD and of the respirable fraction of EC and TC (ECR and TCR) was carried out in the workers’ breathing zone during full-shift work. Regarding ambient measurements, in addition to ECR, TCR and RCD, a submicron aerosol fraction (less than 1 µm) of EC and TC was also sampled (EC1 and TC1). Average ambient results of 240 µg/m3 in RCD, 150 µg/m3 in ECR and 210 µg/m3 in TCR are obtained. Average PBZ results of 190 µg/m3 in RCD, 84 µg/m3 in ECR and 150 µg/m3 in TCR are obtained. Very good correlation is found between ECR and EC1 with a Pearson correlation coefficient of 0.99 (p < 0.01) calculated between the two log-transformed concentrations. No differences are reported between ECR and EC1, nor between TCR and TC1, since ratios are equal to 1.04, close to 1, in both cases. Highest exposures are reported for load-haul-dump (LHD) and jumbo drill operators and conventional miners. Significant exposure differences are reported between mines for truck and LHD operators (p < 0.01). The average TCR/ECR ratio is 1.6 for PBZ results, and 1.3 for ambient results. The variability observed in the TCR/ECR ratio shows that interferences from non-diesel related organic carbon can skew the interpretation of results when relying only on TC data. Keywords: Diesel exposure, Underground mine, Respirable combustible dust, Elemental carbon, Total carbon, Diesel particulate matter, Similar exposure group

Diesel engine exhaust exposure in underground mines: Comparison between different surrogates of particulate exposure

<p>Exposure to diesel particulate matter (DPM) is frequently assessed by measuring indicators of carbon speciation, but these measurements may be affected by organic carbon (OC) interference. Furthermore, there are still questions regarding the reliability of direct-reading instruments (DRI) for measuring DPM, since these instruments are not specific and may be interfered by other aerosol sources. This study aimed to assess DPM exposure in 2 underground mines by filter-based methods and DRI and to assess the relationship between the measures of elemental carbon (EC) and the DRI to verify the association of these instruments to DPM. Filter-based methods of respirable combustible dust (RCD), EC, and total carbon (TC) were used to measure levels of personal and ambient DPM. For ambient measurements, DRI were used to monitor particle number concentration (PNC; PTrak), particle mass concentration (DustTrak DRX and DustTrak 8520), and the submicron fraction of EC (EC_1;Airtec). The association between ambient EC and the DRI was assessed by Spearman correlation. Geometric mean concentrations of RCD, respirable TC (TC_R) and respirable elemental EC (EC_R) were 170 µg/m³, 148 µg/m³, and 83 µg/m³ for personal samples, and 197 µg/m³, 151 µg/m³, and 100 µg/m³ for ambient samples. Personal measurements had higher TC_R:EC_R ratios compared to ambient samples (1.8 vs. 1.50) and weaker association between EC_R and TC_R. Among the DRI, the measures of EC₁ by the Airtec (ρ = 0.86; P < 0.001) and the respirable particles by the DustTrak 8520 (ρ = 0.74; P < 0.001) showed the strongest association with EC, while PNC showed a weak and non-significant association with EC. In conclusion, this study provided important information about the concentrations of DPM in underground mines by measuring several indicators using filter-based methods and DRI. Among the DRI, the Airtec proved to be a good tool for estimating EC concentrations and, although the DustTrak showed good association with EC, interferences from other aerosol sources should be considered when using this instrument to assess DPM.</p