91 research outputs found
Potential impact of autonomous vehicles in mixed traffic from simulation using real traffic flow
No full textThis work focuses on the potential impacts of the autonomous vehicles in a mixed traffic condition represented in traffic simulator Simulation of Urban MObility (SUMO) with real traffic flow. Specifically, real traffic flow and speed data collected in 2002 and 2019 in Gothenburg were used to simulate daily flow variation in SUMO. In order to predict the most likely drawbacks during the transition from a traffic consisting only manually driven vehicles to a traffic consisting only fully-autonomous vehicles, this study focuses on mixed traffic with different percentages of autonomous and manually driven vehicles. To realize this aim, several parameters of the car following and lane change models of autonomous vehicles are investigated in this paper. Along with the fundamental diagram, the number of lane changes and the number of conflicts are analyzed and studied as measures for improving road safety and efficiency. The study highlights that the autonomous vehicles\u27 features that improve safety and efficiency in 100% autonomous and mixed traffic are different, and the ability of autonomous vehicles to switch between mixed and autonomous driving styles, and vice versa depending on the scenario, is necessary
Safety-centred analysis of transition stages to traffic with fully autonomous vehicles
No full textThe aim of this paper is to highlight and investigate the effects of increasing presence rate of autonomous vehicles (AVs) in terms of traffic safety and traffic flow characteristics. For this purpose, using existing driver models in traffic simulator SUMO we identify and analyze those parameters that characterize and distinguish AVs' driving from manual driving in a heterogeneous traffic context. While it is essential to identify the parameters for traffic flow characteristics of heterogeneous fleets compared to homogeneous ones comprising manually driven vehicles (MV) only (i.e. current status), the safety aspects must be also accounted for. In order to combine these two fundamental aspects of heterogeneous traffic, we used a complete description of a highway driving scenario. The scenario integrates the perceptions of different type of vehicles (i.e. AV and MV) involved and the reaction times of human drivers and decision-making units of autonomous vehicles, to explore the impact of both the rate of AV presence and the perturbation in perception capabilities in highway scenarios
A video-driven model of response statistics in the primate middle temporal area
No full textThe final publication is available at Elsevier via https://dx.doi.org/10.1016/j.neunet.2018.09.004 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/Neurons in the primate middle temporal area (MT) encode information about visual motion and binocular disparity. MT has been studied intensively for decades, so there is a great deal of information in the literature about MT neuron tuning. In this study, our goal is to consolidate some of this information into a statistical model of the MT population response. The model accepts arbitrary stereo video as input. It uses computer-vision methods to calculate known correlates of the responses (such as motion velocity), and then predicts activity using a combination of tuning functions that have previously been used to describe data in various experiments. To construct the population response, we also estimate the distributions of many model parameters from data in the electrophysiology literature. We show that the model accounts well for a separate dataset of MT speed tuning that was not used in developing the model. The model may be useful for studying relationships between MT activity and behavior in ethologically relevant tasks. As an example, we show that the model can provide regression targets for internal activity in a deep convolutional network that performs a visual odometry task, so that its representations become more physiologically realistic.MitacsCrossWing In
Mathematical Definitions of Scene and Scenario for Analysis of Automated Driving Systems in Mixed-Traffic Simulations
No full textThis paper introduces a unified mathematical definition for describing commonly used terms encountered in systematical analysis of automated driving systems in mixed-traffic simulations. The most significant contribution of this work is in translating the terms that are clarified previously in literature into a mathematical set and function based format. Our work can be seen as an incremental step towards further formalisation of Domain-Specific-Language (DSL) for scenario representation. We also extended the previous work in the literature to allow more complex scenarios by expanding the model-incompliant information using set-theory to represent the perception capacity of the road-user agents. With this dynamic perception definition, we also support interactive scenarios and are not limited to reactive and pre-defined agent behavior. Our main focus is to give a framework to represent realistic road-user behavior to be used in simulation or computational tool to examine interaction patterns in mixed-traffic conditions. We believe that, by formalising the verbose definitions and extending the previous work in DSL, we can support automatic scenario generation and dynamic/evolving agent behavior models for simulating mixed traffic situations and scenarios. In addition, we can obtain scenarios that are realistic but also can represent rare-conditions that are difficult to extract from field-tests and real driving data repositories
Impact of cognitive load and frustration on drivers’ speech [Abstract]
No full textSecondary tasks such as cell phone calls or interaction with automated speech dialog systems (SDSs) increase the driver’s cognitive load as well as the probability of driving errors. This study analyzes speech production variations due to cognitive load and emotional state of drivers in real driving conditions. Speech samples were acquired from 24 female and 17 male subjects (approximately 8.5 h of data) while talking to a co-driver and communicating with two automated call centers, with emotional states (neutral, negative) and the\ud
number of necessary SDS query repetitions also labeled. A consistent shift in a number of speech production parameters (pitch, first format center frequency, spectral center of gravity, spectral energy spread, and duration of voiced segments) was observed when comparing\ud
SDS interaction against co-driver interaction; further increases were observed when considering negative emotion segments and the number of requested SDS query repetitions. A mel frequency cepstral coefficient based Gaussian mixture classifier trained on 10 male and 10 female sessions provided 91% accuracy in the open test set task of distinguishing co-driver interactions from SDS interactions,\ud
suggesting—together with the acoustic analysis—that it is possible to monitor the level of driver distraction directly from their speech
Vehicle driver vigilance monitoring system design
No full textThe majority of road accidents can be prevented by the passive and active safety designs and systems present in today's modern passenger car and by road infrastructure improvements. However, the large part of the accident risk depends on the driver status and vigilance, no matter how well the car is equipped and how safe the roads are. It is believed that an underestimated 20% of accidents are due to lack of sleep, inattentiveness and lack of vigilance, which can be summarized under impaired driving.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Semantic Analysis of Driver Behavior by Data Fusion
No full textBehavioral signal processing and data-fusion have been two important components of the analytical toolbox that is used to understand driver behavior and implement advanced driver assistance systems (ADAS). The recent need for quantitative analysis of driver behavior is now driven by a new revelation that incorporating human-like behavior and control strategies in the autonomous vehicles can increase their safety and acceptability in a mixed-fleet traffic environment. In addition to that, the overall safety and efficiency of the driver-vehicle system in a conditional or partial automation (Level 2–4) can be leveraged if the perception, cognition, and action capabilities of driver are enhanced based on driving-task or traffic-scenario. Motivated by this new interest, this work attempts to define a highlevel semantic analysis framework incorporating eye-motion, road-scene, and vehicle dynamics data. The study aims to identify general trends or patterns in driver behavior, especially concerning focus of attention (FoA), based on two categories: traffic scenario and complexity. To perform semantic analysis, open database from DR(eye)VE Project is used. First, the road-scene video and vehicle dynamics data are used together to obtain a complexity measure in addition to automatic recognition of the traffic-scenario. Next, the raw eye-movement data is processed to obtain gaze distribution maps and metrics. Then, a support vector machine (SVM) is trained using gaze metrics to infer the complexity level or the traffic-scenario. To obtain better separation between two classes (i.e., low vs high complexity or urban vs highway scenarios), the SVM is trained using Bayesian optimization. The results showed that based on the gaze distribution, it is possible to distinguish between urban and highway scenarios (85% accuracy), while this distinction between complexity levels can be even stronger (98% accuracy). The framework can be used as a high-level analysis and inference tool to discover behavioral characteristics of drivers and their relation to FoA patterns
Evaluation of gastronomy proceeding papers: The case of tourism congresses, 2013-2017
No full textBu araştırmanın amacı Türkiye’de turizm ana temalı kongrelerde gastronomi alanında yazılmış bildirilerin bibliyometrik olarak incelenmesidir. Araştırmanın evrenini, Türkiye’de 2013-2017 yılları arasında düzenlenmiş turizm kongrelerinde sözlü olarak sunulan tam metin bildiriler oluşturmaktadır. Nitel olarak tasarlanan araştırmada verilerin analizi için betimsel analiz yöntemi benimsenmiştir. Bildiriler istatistik paket programı aracılığıyla sıklık ve yüzde analizlerine tabi tutularak belirli bibliyometrik parametreler açısından ele alınmıştır. Araştırma ile 5 yıllık dönemde 26 farklı turizm kongresinde gastronomi temalı 285 bildiri tespit edilmiş ve bu bildirilerin ortaya konmasında 682 farklı yazarın katkı sunduğu belirlenmiştir. Kongrelere en çok bildiri ile katkı sağlayan yazarın Aydan Bekar ve bildirilere en çok katkı sağlayan üniversitenin yazar sayısı bakımından Balıkesir Üniversitesi, bildiri sayısı açısından ise Mersin Üniversitesi olduğu; yaklaşık %87,4’ünün en az iki yazar tarafından ortak olarak gerçekleştirildiği; bildirilerin %35,1’inin en az iki farklı üniversitede çalışan yazarlar tarafından üniversiteler arası akademik araştırma olarak tamamlandığı belirlenmiştir. Bildirilerin en çok “Gastronomi Turizmi Pazarlaması” alanında yazıldığı tespit edilmiştir.The aim of research is investigating bibliometrically proceedings written in the field of
gastronomy in tourism within the scope of the research. The research population consists of
full text papers that are presented in tourism congress between the years of 2013-2017 in
Turkey. In this qualitatively designed research, descriptive analysis has been adopted for
the accessed data. Papers were analyzed in terms of specific bibliometric parameters by
frequency and percent analysis through statistical package program. 285 papers on
gastronomic themes were found in 26 different tourism congresses in 5 years’ period, with
an average of 11 papers per congress and 57 papers per year with the research; and it was
determined 682 different authors contributed to the presentation of them. The results show
that Aydan Bekar as an author, in terms of the number of authors Balıkesir University and
in terms of the number of papers Mersin University has contributed most to the congresses;
about 87.4% of the papers were made cooperatively by at least two authors; 35.1% of them
have been completed as academic research between universities by authors working in at
least two different universities. It has been found that the most of the gastronomic themes
discussed in the papers are in the field of “Gastronomy Tourism Marketing”
Acoustic road-type estimation for intelligent vehicle safety applications
No full textA low-cost acoustic road-type classification system is proposed to be used in road-tyre friction force estimation in active safety applications. The system employs audio signal processing and extracts features such as linear predictive coefficients (LPC), mel-frequency cepstrum coefficients (MFCC) and power spectrum coefficients (PSC). The features are extracted using time windows of 0.02, 0.05 and 0.1 seconds in order to find the best representative window for the signal properties which should also be as short as possible for active safety systems. In order to find the best feature space, a variance analysis based approach is considered to represent the road types as distinguished classes. Optimised feature space is classified using artificial neural networks (ANN). The results show that the designed ANN can classify the road types with 91% accuracy at worst condition. To demonstrate the value of the system, a case study including traction control application is reported
Evaluation of gastronomy proceeding papers in tourism congresses
No full textBu araştırmanın amacı turizmde gastronomi alanında yazılmış bildirilerin bibliyometrik olarak incelenmesidir. Araştırmanın evrenini, Türkiye’de 2013-2017 yılları arasında gerek periyodik olarak gerek en fazla iki defa düzenlenmiş periyodik olmayan turizm kongrelerinde sözlü olarak sunulan tam metin bildiriler oluşturmaktadır. Nitel olarak tasarlanan araştırmada verilerin analizi için betimsel analiz yöntemi benimsenmiştir. Bildiriler istatistik paket programı aracılığıyla sıklık ve yüzde analizlerine tabi tutularak belirli bibliyometrik parametreler açısından ele alınmıştır. Araştırma ile 5 yıllık periyotta 26 farklı turizm kongresinde gastronomi temalı 285 bildiri tespit edilmiş, kongre bazında ortalama 11 ve yıl başına ortalama 57 bildirinin düştüğü, bu bildirilerin ortaya konmasında 682 farklı yazarın katkı sunduğu belirlenmiştir. Kongrelere en çok bildiri ile katkı sağlayan yazarın Aydan Bekar ve bildirilere en çok katkı sağlayan üniversitenin yazar sayısı bakımından Balıkesir Üniversitesi, bildiri sayısı açısından ise Mersin Üniversitesi olduğu; yaklaşık %87,4’ünün en az iki yazar tarafından ortak olarak gerçekleştirildiği; bildirilerin %35,1’inin en az iki farklı üniversitede çalışan yazarlar tarafından üniversiteler arası akademik araştırma olarak tamamlandığı belirlenmiştir. Bildirilerde ele alınan gastronomi temalarının sırasıyla en çok “Gastronomi Turizmi Pazarlaması”, “Gastronomik Miras” ve “Gastronomik Ürünler” alanlarında yazıldığı tespit edilmiştir. Veri toplama yöntemi açısından bildirilerde nitel araştırma yöntemi ile görüşme(mülakat) tekniğinin ağırlıklı olarak kullanıldığı; yaklaşım bakımından çoğunluğunun keşifsel olarak tasarlandığı; alan araştırması olması yönünden yarısından fazlasının uygulamalı olarak gerçekleştirildiği; veri analizinde ise %72,7 betimsel ve %27,3 ilişkisel istatistikler kullanıldığı belirlenmiştir. Son olarak ise bildirilerde ortalama 27 kaynak kullanıldığı; %61’inin Türkçe, %39’unun yabancı, % 49,8’inin makale türünde ve kaynaklarının ortalama referans yaşının ise 8,1 olduğu sonucuna ulaşılmıştır.The aim of research is investigating bibliometrically proceedings written in the field of gastronomy in tourism
within the scope of the research. The research population consists of full text papers that are presented in tourism
congress between the years of 2013-2017 in Turkey. In this qualitatively designed research, descriptive analysis
has been adopted for the accessed data. Papers were analyzed in terms of specific bibliometric parameters by
frequency and percent analysis through statistical package program. 285 papers on gastronomic themes were
found in 26 different tourism congresses in 5 years’ period, with an average of 11 papers per congress and 57
papers per year with the research; and it was determined 682 different authors contributed to the presentation of
them. The results show that Aydin Bekar as an author, in terms of the number of authors Balıkesir University
and in terms of the number of papers Mersin University has contributed most to the congresses; about 87.4% of
the papers were made cooperatively by at least two authors; 35.1% of them have been completed as academic
research between universities by authors working in at least two different universities. It has been found that the
most of the gastronomic themes discussed in the papers are in the fields of “Gastronomy Tourism Marketing”,
“Gastronomic Heritage” and “Gastronomic Products”. In terms of data collection method, qualitative research
method and interview technique are mainly used; in terms of approach the majority is explorative designed;
more than half of the papers written as in the field research; and in terms of data analysis, it was determined that
72.7% descriptive and 27.3% relational statistics were used for papers. Finally, an average of 27 sources were
used in papers; 61% in Turkish, 39% in foreign language, 49.8% in article type, and the average reference age of
resources calculated as 8,1
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