Multisensory cues can facilitate or impair driving performance depending on their congruency. The document proposes an experiment to test this using a lane change test. It involves presenting visual lane change cues with concurrent auditory cues varying in spatial, temporal and semantic congruency. Response times will be measured to see how congruent and incongruent multisensory cues impact driving performance compared to visual-only cues. The results could help understand how to best design in-vehicle multimodal displays.
CONSIDERATION OF HUMAN COMPUTER INTERACTION IN ROBOTIC FIELD ijcsit
Technological progress leads the apparition of robot in human environment; we began to find them in hospitals, museums, and homes. However these situations require an interaction of robots with humans and an adoption of social behaviors. We have shown in this paper how disciplines like computer science in general and human computer interaction in particular are used to improve human robot interaction. Then we indicated how we can use action theory into design of interaction between human and Robot. Finally we proposed some practical scenarios for illustrations.
Review Paper on Intelligent Traffic Control system using Computer Vision for ...JANAK TRIVEDI
In today scenario city will try to modify in the form of smart city with better facilities in terms of education, social-economic life,
better transportation availability, noise free – Eco-friendly environment availability, and ICT- Information and communication technology
enabler for development in the city. In this paper, we are reviewing different work already done or draft by some research in the field of traffic
control system – for better monitoring, tracking and managing using a computer vision system. Nowadays, most of the city installed with
C.C.T.V. – camera for monitoring the traffic related activity.
A contextual bandit algorithm for mobile context-aware recommender systemBouneffouf Djallel
Most existing approaches in Mobile Context-Aware Recommender Systems focus on recommending relevant items to users taking into account contextual information, such as time, location, or social aspects. However, none of them has considered the problem of user’s content evolution. We introduce in this paper an algorithm that tackles this dynamicity. It is based on dynamic exploration/exploitation and can adaptively balance the two aspects by deciding which user’s situation is most relevant for exploration or exploitation. Within a deliberately designed offline simulation framework we conduct evaluations with real online event log data. The experimental results demonstrate that our algorithm outperforms surveyed algorithms.
Exploration exploitation trade off in mobile context-aware recommender systemsBouneffouf Djallel
Most existing approaches in Context-Aware Recommender Systems (CRS) focus on recommending relevant items to users taking into account contextual information, such as time, loca-tion, or social aspects. However, none of them have considered the problem of user’s content dynamicity. This problem has been studied in the reinforcement learning community, but without paying much attention to the contextual aspect of the recommendation. We introduce in this paper an algorithm that tackles the user’s content dynamicity by modeling the CRS as a contextual bandit algorithm. It is based on dynamic explora-tion/exploitation and it includes a metric to decide which user’s situation is the most relevant to exploration or exploitation. Within a deliberately designed offline simulation framework, we conduct extensive evaluations with real online event log data. The experimental results and detailed analysis demon-strate that our algorithm outperforms surveyed algorithms.
CONSIDERATION OF HUMAN COMPUTER INTERACTION IN ROBOTIC FIELD ijcsit
Technological progress leads the apparition of robot in human environment; we began to find them in hospitals, museums, and homes. However these situations require an interaction of robots with humans and an adoption of social behaviors. We have shown in this paper how disciplines like computer science in general and human computer interaction in particular are used to improve human robot interaction. Then we indicated how we can use action theory into design of interaction between human and Robot. Finally we proposed some practical scenarios for illustrations.
Review Paper on Intelligent Traffic Control system using Computer Vision for ...JANAK TRIVEDI
In today scenario city will try to modify in the form of smart city with better facilities in terms of education, social-economic life,
better transportation availability, noise free – Eco-friendly environment availability, and ICT- Information and communication technology
enabler for development in the city. In this paper, we are reviewing different work already done or draft by some research in the field of traffic
control system – for better monitoring, tracking and managing using a computer vision system. Nowadays, most of the city installed with
C.C.T.V. – camera for monitoring the traffic related activity.
A contextual bandit algorithm for mobile context-aware recommender systemBouneffouf Djallel
Most existing approaches in Mobile Context-Aware Recommender Systems focus on recommending relevant items to users taking into account contextual information, such as time, location, or social aspects. However, none of them has considered the problem of user’s content evolution. We introduce in this paper an algorithm that tackles this dynamicity. It is based on dynamic exploration/exploitation and can adaptively balance the two aspects by deciding which user’s situation is most relevant for exploration or exploitation. Within a deliberately designed offline simulation framework we conduct evaluations with real online event log data. The experimental results demonstrate that our algorithm outperforms surveyed algorithms.
Exploration exploitation trade off in mobile context-aware recommender systemsBouneffouf Djallel
Most existing approaches in Context-Aware Recommender Systems (CRS) focus on recommending relevant items to users taking into account contextual information, such as time, loca-tion, or social aspects. However, none of them have considered the problem of user’s content dynamicity. This problem has been studied in the reinforcement learning community, but without paying much attention to the contextual aspect of the recommendation. We introduce in this paper an algorithm that tackles the user’s content dynamicity by modeling the CRS as a contextual bandit algorithm. It is based on dynamic explora-tion/exploitation and it includes a metric to decide which user’s situation is the most relevant to exploration or exploitation. Within a deliberately designed offline simulation framework, we conduct extensive evaluations with real online event log data. The experimental results and detailed analysis demon-strate that our algorithm outperforms surveyed algorithms.
We were invited to speak at a meeting of the student chapter of IGAEA at UH on April 23, 2013. We talked about who we are, what we do, and how we got here. Then we gave advice to students about how to make their degree more valuable and provided some resources on how to do that.
7 visbiežāk pieļautās kļūdas e-pasta mārketingā Mailigen_lv
Esam apkopojuši septiņas izplatītākās e-pasta mārketinga kļūdas, no kurām jāizvairās, lai e-pasta kampaņu izsūtīšana noritētu bez aizķeršanās.
Prezentācija no Mailigen Workshop 2014
Anna Ovčiņņikova, Mailigen atbalsta menedžere
عنوان الكتاب: منهج الشيخ محمد رشيد رضا في العقيدة
المؤلف: تامر محمد محمود متولي
الناشر: دار ماجد عسيري
سنة النشر: 1425 - 2004
عدد المجلدات: 1
نبذة عن الكتاب:
الطبعة الأولى
985 صفحة
منهج الحافظ ابن حجر العسقلاني في العقيدة من خلال كتابه - فتح الباريOm Muktar
عنوان الكتاب: منهج ابن حجر العسقلاني في العقيدة من خلال كتاب فتح الباري
المؤلف: محمد إسحاق كندو
نبذة عن الكتاب: مكتبة الرشد - الرياض - 3 مجلداتن - 1590 صفحة - 16 ميجا
Fuzzy Logic Model for Traffic CongestionIOSR Journals
Abstract: Traffic congestion has become a serious problem in the urban districts. This is mainly due to the
rapid increase in the number and the use of vehicles. Travel time, travel safety, environmental quality, and life
quality are all adversely affected by traffic congestion. Many traffic control systems have been developed and
installed to alleviate the problem with limited success. Traffic demands are still high and increasing. The main
focus of this report is to introduce a versatile fuzzy logic traffic flow model capable of making optimal traffic
predictions. This model can be used to evaluate various traffic-light timing plans. More importantly, it provides
a framework for implementing adaptive traffic signal controllers based on fuzzy logic technology. When
implemented it solved the problem of waiting time, travel cost, accident, traffic congestion.
Key words: Traffic Congestion, fuzzy logic, Traffic Density, fuzzy controller, conventional controller.
Study of Congestion Control Scheme with Decentralized Threshold Function in V...ijtsrd
With the constant increase in vehicular traffic, existing traffic management solutions have become inefficient. Urbanization has led to an increase in traffic jams and accidents in major cities. In order to accommodate the growing needs of transport systems today, there is a need for an Intelligent Transport System. Vehicular Ad hoc Network VANET is a growing technology that assists in Intelligent Transport Systems. VANETs enable communication between vehicles as well as fixed infrastructure called Road Side Units RSU . We propose a distributed, collaborative traffic congestion detection and dissemination system that uses VANET. Each of the driver's smart phones is equipped with a Traffic App which is capable of location detection through Geographic Position based System GPS . This information is relayed to a remote server which detects traffic congestion. Once congestion is confirmed the congestion information is disseminated to the end user phone through RSUs. The Mobile App transmits the location information at periodic intervals. Using the latitude, longitude and the current time, the location of each vehicle is traced. Using location information, the distance moved by the vehicle at a given time is monitored. If the value is below a fixed threshold, congestion is suspected in a particular area. If many vehicles in the same area send similar messages, traffic congestion is confirmed. Once traffic congestion is confirmed, the vehicles approaching the congested area are informed about the traffic through display boards that are available in the nearest RSUs traffic signals . The congestion information is also made available through the Mobile App present in vehicles approaching the congested area. The approaching vehicles may take diversion and alleviate congestion. Anees Khan | Prof. Sarwes Site "Study of Congestion Control Scheme with Decentralized Threshold Function in VANETs" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25324.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/25324/study-of-congestion-control-scheme-with-decentralized-threshold-function-in-vanets/anees-khan
Traffic Congestion Prediction using Deep Reinforcement Learning in Vehicular ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
TRAFFIC CONGESTION PREDICTION USING DEEP REINFORCEMENT LEARNING IN VEHICULAR ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
Crowd Recognition System Based on Optical Flow Along with SVM classifierIJECEIAES
The manuscript discusses about abnormalities in a crowded scenario. To prevent the mishap at a public place, there is no much mechanism which could prevent or alert the concerned authority about suspects in a crowd. Usually in a crowded scene, there are chances of some mishap like a terrorist attack or a crime. Our target is finding techniques to identify such activities and to possibly prevent them. If the crowd members exhibit abnormal behavior, we could identify and say that this particular person is a suspect and then the concerned authority would look into the matter. There are various methods to identify the abnormal behavior. The proposed approach is based on optical flow model. It has an ability to detect the sudden changes in motion of an individual among the crowd. First, the main region of motion is extracted by the help of motion heat map. Harris corner detector is used for extracting point of interest of extracted motion area. Based on the point of interest an optical flow is estimated here. After analyzing this optical flow model, a threshold value is fixed. Basically optical flow is an energy level of individual frame. The threshold value is forwarded to SVM classifier, which produces a better result with 99.71% accuracy. This approach is very useful in real time video surveillance system where a machine can monitor unwanted crowd activity.
We were invited to speak at a meeting of the student chapter of IGAEA at UH on April 23, 2013. We talked about who we are, what we do, and how we got here. Then we gave advice to students about how to make their degree more valuable and provided some resources on how to do that.
7 visbiežāk pieļautās kļūdas e-pasta mārketingā Mailigen_lv
Esam apkopojuši septiņas izplatītākās e-pasta mārketinga kļūdas, no kurām jāizvairās, lai e-pasta kampaņu izsūtīšana noritētu bez aizķeršanās.
Prezentācija no Mailigen Workshop 2014
Anna Ovčiņņikova, Mailigen atbalsta menedžere
عنوان الكتاب: منهج الشيخ محمد رشيد رضا في العقيدة
المؤلف: تامر محمد محمود متولي
الناشر: دار ماجد عسيري
سنة النشر: 1425 - 2004
عدد المجلدات: 1
نبذة عن الكتاب:
الطبعة الأولى
985 صفحة
منهج الحافظ ابن حجر العسقلاني في العقيدة من خلال كتابه - فتح الباريOm Muktar
عنوان الكتاب: منهج ابن حجر العسقلاني في العقيدة من خلال كتاب فتح الباري
المؤلف: محمد إسحاق كندو
نبذة عن الكتاب: مكتبة الرشد - الرياض - 3 مجلداتن - 1590 صفحة - 16 ميجا
Fuzzy Logic Model for Traffic CongestionIOSR Journals
Abstract: Traffic congestion has become a serious problem in the urban districts. This is mainly due to the
rapid increase in the number and the use of vehicles. Travel time, travel safety, environmental quality, and life
quality are all adversely affected by traffic congestion. Many traffic control systems have been developed and
installed to alleviate the problem with limited success. Traffic demands are still high and increasing. The main
focus of this report is to introduce a versatile fuzzy logic traffic flow model capable of making optimal traffic
predictions. This model can be used to evaluate various traffic-light timing plans. More importantly, it provides
a framework for implementing adaptive traffic signal controllers based on fuzzy logic technology. When
implemented it solved the problem of waiting time, travel cost, accident, traffic congestion.
Key words: Traffic Congestion, fuzzy logic, Traffic Density, fuzzy controller, conventional controller.
Study of Congestion Control Scheme with Decentralized Threshold Function in V...ijtsrd
With the constant increase in vehicular traffic, existing traffic management solutions have become inefficient. Urbanization has led to an increase in traffic jams and accidents in major cities. In order to accommodate the growing needs of transport systems today, there is a need for an Intelligent Transport System. Vehicular Ad hoc Network VANET is a growing technology that assists in Intelligent Transport Systems. VANETs enable communication between vehicles as well as fixed infrastructure called Road Side Units RSU . We propose a distributed, collaborative traffic congestion detection and dissemination system that uses VANET. Each of the driver's smart phones is equipped with a Traffic App which is capable of location detection through Geographic Position based System GPS . This information is relayed to a remote server which detects traffic congestion. Once congestion is confirmed the congestion information is disseminated to the end user phone through RSUs. The Mobile App transmits the location information at periodic intervals. Using the latitude, longitude and the current time, the location of each vehicle is traced. Using location information, the distance moved by the vehicle at a given time is monitored. If the value is below a fixed threshold, congestion is suspected in a particular area. If many vehicles in the same area send similar messages, traffic congestion is confirmed. Once traffic congestion is confirmed, the vehicles approaching the congested area are informed about the traffic through display boards that are available in the nearest RSUs traffic signals . The congestion information is also made available through the Mobile App present in vehicles approaching the congested area. The approaching vehicles may take diversion and alleviate congestion. Anees Khan | Prof. Sarwes Site "Study of Congestion Control Scheme with Decentralized Threshold Function in VANETs" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25324.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/25324/study-of-congestion-control-scheme-with-decentralized-threshold-function-in-vanets/anees-khan
Traffic Congestion Prediction using Deep Reinforcement Learning in Vehicular ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
TRAFFIC CONGESTION PREDICTION USING DEEP REINFORCEMENT LEARNING IN VEHICULAR ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
Crowd Recognition System Based on Optical Flow Along with SVM classifierIJECEIAES
The manuscript discusses about abnormalities in a crowded scenario. To prevent the mishap at a public place, there is no much mechanism which could prevent or alert the concerned authority about suspects in a crowd. Usually in a crowded scene, there are chances of some mishap like a terrorist attack or a crime. Our target is finding techniques to identify such activities and to possibly prevent them. If the crowd members exhibit abnormal behavior, we could identify and say that this particular person is a suspect and then the concerned authority would look into the matter. There are various methods to identify the abnormal behavior. The proposed approach is based on optical flow model. It has an ability to detect the sudden changes in motion of an individual among the crowd. First, the main region of motion is extracted by the help of motion heat map. Harris corner detector is used for extracting point of interest of extracted motion area. Based on the point of interest an optical flow is estimated here. After analyzing this optical flow model, a threshold value is fixed. Basically optical flow is an energy level of individual frame. The threshold value is forwarded to SVM classifier, which produces a better result with 99.71% accuracy. This approach is very useful in real time video surveillance system where a machine can monitor unwanted crowd activity.
Effects of mobility models and nodes distribution on wireless sensors networksijasuc
Wireless sensor networks (WSN) is an important future technology, in several applications in military,
health, environment and industries. Currently the integration of social and sensor is very important by
considering the characteristics of social networks in designing wireless sensor networks WSN for
improvement such as (number of messages from source to destination, radius of coverage, connectivity, and
spreading). This area has not received much attention and few researches focus on the performance
evaluation. In this paper we have studied the impact of different mobility and distribution models which is a
variable one should define which model is best for the infrastructure given their differences, also study
include the exact effect of nodes distribution and analyzed by calculation the number of messages of 12
cases to get a real performance evaluation under different conditions and same routing techniques. This
work provides us a greater understanding and clear an idea of the effect of mobility plus distribution.
Impact of different mobility scenarios on fqm framework for supporting multim...ijwmn
In Mobile Ad hoc Network (MANET), the mobility of nodes is a challenging issue for designers. There are
lots of possibilities of mobile scenarios in this kind of network. The source, destinations and intermediate
nodes may not be using the same mobile scenarios. In this study, three mobile scenarios are taken in
consideration and these scenarios are source mobility, destinations mobility and intermediate nodes
mobility. The impact of the three mobile scenarios on the Quality of service Multicast Framework (FQM)
for supporting multimedia applications in MANETs is studied. The simulation results show that mobility of
group of destinations affects the performance of FQM framework more than mobility of source. In addition,
the analysis of simulation results shows that mobility of intermediate nodes does not have high effect on the
performance of FQM framework when node density is not high.
Thesis Blurb intro-Multisensory Warning Cue Evaluation in Driving-Jan18
1. Background and Intro
Multisensory Cue Congruency in
Lane Change Test
Yuanjing Sun
Advisor: Myounghoon Jeon
Dec 4th ,2015
Mobile internet extends interaction with web applications to driver’s seat. Multitasking while
driving becomes an inevitable challenge to not only drivers, but also the whole automobile
industry.
Even though 14 states prohibit drivers from using hand-held cell phones while driving, a large
portion of injury crashes has been defined as distraction-related crashes (Nhtsa.gov, 2015). So,
many institutes and organizations have tried to develop well-designed in-vehicle technologies to
support driving tasks.
1.1 Background
1/18/2016 3
IVIS assistance should not cause overload!!
Wickens’ Multiple Resource Theory
• Extends information receiving channels beyond
vision
Reliability issues of research protocols
• Various research protocols and simulators
• Adaptive Integrated Driver-vehicle interface” (AIDE)
(Engström et al., 2004)
• a U.S. project, “SAfety VEhicle using adaptive Interface
Technology” (SAVE-IT)
2. Multiple resource theory (MRT) describes how information travels through different channels in
our brain depending on its modalities. (Wickens, 2008). MRT suggested a way to provide as much
information to drivers as workload permits. Well-designed multimodal interface can extend
information-receiving channels beyond vision. For example, speech recognition and seat
vibrotactile notifications tend to become popular in recent car generations. However, we need to
identify the strengths and weaknesses of multimodal interfaces.
There have been efforts to construct methodologies to measure driving distraction from
interaction with IVIS. But previous studies vary in experimental settings in terms of different
research protocols and different simulators. So, there are some reliability issues.
1.2 Why Use the Lane-Change Test (LCT)?
• The Lane Change Test (ISO26022) is a simple laboratory dual task
method that quantitatively measures performance impairment in a
primary driving task (Mattes,2003).
• Economical, low-fidelity driving simulator
• Previous data provides high validity
• Discrete event under continuous visual task
• Decompose driving performance into event detection (RT) and lateral
maneuver (lane deviation), which provides a speed-accuracy tradeoff
1/18/2016 4/30
The Lane Change Test compensates for these issues because it is a standardized methodology to
measure distraction. It is an easy-to-use laboratory method that can quantify the distraction by
measuring the performance impairment of the primary driving task.
3. 1.3 Motivation: How to test if IVISs facilitate or
distract drivers?
•How will spatially or temporally incongruent audio-
visual cues impact driving performance?
• Auditory Spatial Stroop (Baldwin, 2012) experiment
to measure the variance of driving performance under
multimodal cue combinations.
1/18/2016 5/30
Although IVIS provides driving related information, it still occupies part of attentional resources.
It is hard to tell whether this so-called assistance will facilitate or distract driving performance
because there is still a gap in evaluation of IVIS utility. Thus, I propose an Auditory Spatial Stroop
experiment to investigate how driving performance varies under different multimodal cue
combinations.
The Auditory Spatial Stroop experiment investigates whether the location or the meaning of the
stimuli more strongly influences performance when they conflict with each other. For example,
the word “LEFT” or “RIGHT” is presented in a corresponding or opposite position from its meaning.
It simulates the complex driving environment: For example, your navigation device tell you to turn
right but the collision avoidance system warns you that some hazard is coming from right.
Literature Review
The information processing framework can be divided into various sub-processes depending on
different perspectives. Both multimodal/crossmodal facilitation and inhibition have been studied
with different theories and mechanisms. I will review models and theories that are closely related
to this proposal. However, note that those are not intended to be exhaustive.
4. 2.1 Hierarchical Driving Behavior Model
• Michon’s hierarchy model (1985)
1/18/2016 7/30
The strategic level e.g., plan route according to traffic
information.
The maneuver level e.g., negotiating curves,
intersections, performing lane change maneuvers and
overtaking, and obstacle avoidance.
The operational level e.g., brake or shifting two choice
single tasks. They are more of a reflexive car control.
Because driving is such a complicated task, Michon divided driving into the three levels. From
highest to lowest are the strategic level, the maneuver level, and the operational level.
The strategic level involves general trip planning, including selecting and evaluating the cost and
risk associated with alternative trips.
The maneuver level involves negotiation of common driving situations (e.g., negotiating curves,
intersections, gap acceptance in overtaking or entering the traffic stream, performing lane change
maneuvers, and obstacle avoidance).
The lowest operational level involves a single task, such as braking, shifting, etc.,.
The present proposal focuses on the maneuver level behavior because it requires perceptually
processed signals, and the integration of visual and spatial information in the driving
environment. The impact of multimodal representation in design of IVIS will be directly
reflected in the maneuver level performance.
2.2 Wickens’ Multiple Resource Theory (MRT)
1/18/2016 8/30
Figure 1.The three dimensional (cube) structure of the Multiple Resource model
(Wickens et al., 2013).
5. Wickens’ (Wickens et al., 2013) multiple resource theory (MRT) is a model to predict interference
between two concurrently presented signals. It is composed of four dimensions as figure one
shows. The four dimensions are stages, modalities, accesses (i.e. “codes” in earlier version) and
responses.
The MRT suggested that two tasks demanding separate resources along these four dichotomous
dimensions will improve the overall time-sharing performance. And it less impairs either task than
those tasks occupying the same resources. In this big model, my study only focuses on this part,
which is related to verbal and spatial codes, and audio / visual modalities.
Facilitation
• Lip-reading
• Crossmodal synesthesia
Inhibition
• Multisensory illusion
- (e.g., McGurk effect)
1/18/2016 9/30
How will multimodal cues have benefits over unimodal cues?
• Spatial rule
• Temporal rule
Synchrony benefit in synesthesia vs. Asynchrony benefit in Posner
(1973) preparation function
2.3 Rules for Crossmodal Facilitation
However, multimodal time sharing is not always good. Researchers find that crossmodal signals
can both benefit and deteriorate information processing. It can be challenged by inhibition
effects such as multisensory illusions. In audiovisual speech studies, it’s generally believed that
lip-reading can enhance the comprehension of the speech in a noise background. However, the
McGurk effect is an exception.
The McGurk illusion (McGurk & MacDonald 1976) described a phenomenon that a sound of /ba/
tends to be perceived as /da/ when it is paired with a visual lip movement /da/. Incongruent
audiovisual cues might inhibit multimodal processing. Here comes the question, “How will
multimodal cues benefit over unimodal?”
Crossmodal synesthesia described a condition in which a person experiences sensations in one
modality when a second modality is stimulated (Olsheski, 2014). For example, there is one spatial
rule; and one temporal rule (Baldwin, 2012, p. 191) to facilitate crossmodal benefits.
Spatial rule In Spence’s (2010) review of crossmodal spatial attention, the spatial rule was defined
as the RT performance benefit on ipsilateral cues over contralateral cues for visual and auditory
modalities. If audio and visual signal comes from same side, it is more likely to be integrated as
one.
Temporal rule
6. The temporal rule outlines that responses to multimodal cues will benefit from temporal
synchrony between visual and auditory cues because of the maximum overlap. However, the
synchrony benefits may not explain every case. Posner, Klein, Summers, and Buggie (1973) asked
participants to respond with a left or right key to a target either occurring left or right to a fixation.
Their study proposed a ‘‘preparation function.’’ It suggested that the response time will be as a
function of the SOA between the priming tone and the visual target stimulus. 200ms SOA showed
the best performance. (So both synchrony and asynchrony will both benefit multisensory
processing, how can we chose in IVIS design?)
2.4 Temporal Rule (cont'd) : Colavita Bias and
SOA for Corssmodal Facilitation
Colavita bias (minimum for SOA)
• Participants respond more often to the visual component instead of
the auditory one. Vision is dominant modality in audiovisual
perception.
Unity effect (maximum for SOA)
• Cue-target pair can be perceived as one integrated simultaneous
attention or two separate multitasking events.
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The SOA affects the occurrence and strength of crossmodal binding.Can auditory cues and visual
cues be processed equally?
The Colavita bias is a visual dominance phenomenon where participants respond more often to
the visual cue instead of the auditory cue even when the two stimuli are presented at the same
time. The neglect of the auditory cue might cause a misjudgment of the prior-entry modality that
is against the researcher’s original intention.
To avoid the Colavita bias, we need a minimum SOA. On the other side, if the SOA is too large
then the cue will be perceived as two separate events. If the SOA is appropriate, it will lead to
simultaneous attention, but if SOA is too large, it will become multitasking. My study focuses on
simultaneous attention, instead of multitasking. In particular, participant will sense two cues as
one unified event.
7. 2.5 Type & Demand of Visual Tasks
Type
• Visual scanning (discrete) task vs. Visual tracking (continuous) task
• A meta-analysis compared auditory-visual (AV) with visual-visual (VV)
tasks.
• AV has 15% advantage over VV in average across 29 studies when there is a
discrete task (Wickens et al., 2011).
• Driving is a visual tracking task. But the Lane-Change Test is a discrete task.
So auditory cues will facilitate performance.
Demand
• Perceptual load (frequency of visual targets) and working memory
load (alternative numbers of response)
• Both multisensory facilitation and inhibition can be demonstrated by
changing the task type and visual demand.
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Perception has a limited capacity but processes stimuli in an automatic and voluntary fashion
until the free capacity is drained out.
Wickens et al. (2013) suggested a crossmodal display may benefit a visual scanning task but not
the continuous visual tracking task. A meta-analysis compared auditory-visual (AV) tasks with
visual-visual (VV) tasks. The results indicated that the auditory presentation offered a 15 percent
advantage (collapsed over both speed and accuracy) over visual only presentation when the task
is discrete.
The level of visual task demand can influences crossmodal facilitation.Sinnett, Soto-Faraco, and
Spence (2008) manipulated perceptual load (frequency of visual targets) and working memory
load (alternative numbers of response) to compare the crossmodel benefit by manipulating these
two variables. Same crossmodal stimuli can either facilitate or inhibit by changing the task type
and visual demand. That’s why I need to control the speed in my experiment
It serves two purposes; 1) It controls the perceptual load. 2) It controls for individual differences
in driving style. Otherwise the facilitation and inhibition will cancel each other out.
Hypothesis
Based on all this background, the proposed study aims to investigate how congruent or
incongruent (temporal, spatial, & semantic) multimodal cues (auditory & visual) influence driving
performance. To test this more, I will use LCT as a surrogate driving task.
8. 3.2 Hypotheses
• H1A: Congruent crossmodal cue-target pairs will have shorter RT than those in
unimodal (visual-only) conditions.
• H1B: Incongruent crossmodal cue-target pairs will have longer RT than unimodal
(visual-only) conditions.
• H1C: Congruent crossmodal pairs will have shorter RT than incongruent crossmodal
pairs.
• H2A: Asynchronous pairs will have shorter RT than unimodal (visual-only) conditions.
• H2B: RT with synchronous crossmodal cues will not be longer than those in unimodal
(visual-only) conditions.
• H3A: When verbal cues are spatially incongruent with visual target, they will delay RT.
• H3B: When verbal cues are spatially incongruent but semantically congruent with
visual target, RT will still be slower than unimodal (visual-only) conditions.
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Experiment Method
4.1 Participants
• Forty participants (20 male and 20 female) will be recruited from
MTU SONA system.
• 18 year-old native speaker who having driving license more than 2
years
• Equivalent hearing test will be given in a training track before the real
experiment starts.
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9. 4.2 Stimuli
• Visual cues are composed of an arrow sign and cross sign as above
• Auditory cues are composed of four nonverbal cues and four verbal
cues
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START
"Start" sign Arrow sign Cross sign
Left right Lef-left Righ-right
Nonverbal cues
Verbal cues
The "Lane Change" signs appear in an overhead position of a gate on the simulated roadway. ("They
are composed of one arrow sign and two cross signs in three separate black borders. There is one “START” sign and one “FINISH”
sign in the beginning and end of each track. )
Two non-verbal stimuli and four verbal stimuli will be used as auditory cues. The nonverbal cues
will be produced with 500Hz 350 msec duration. The nonverbal cues will be presented either from
left or right. It will be either congruent or incongruent depend on the trial.The repeated sound
move two lanes instead of one.(i.e., from left most lane to right most lane or vice versa).
The four verbal cues are “LEFT” “RIGHT”, “LEF-LEFT” and “RIGH-RIGHT”. And the verbal cues will
be produced with the same length and loudness as non-verbal cues. (Chan & Or, 2012). They will
be delived through Headset. And I controlled for cue length and volume.
(The speech clips “LEFT” and “RIGHT” were recorded through free online Text-to-Speech (TTS)
service (Fromtexttospeech.com, 2015) I chose a medium speed with female voice (Laura, US
English) as original speech file. Sped-up speech clips, “LEF-LEFT” and “RIGH-RIGHT” I imported
the original TTS files to Audacity 2.1.0 version and replicated each word to two tracks. For the
first track, the first vowel was reserved, and for the second track the second vowel was reserved.
The last step was to twist the pitch in order to make the duration of two tracks within 350 msec.
Verbal cues have two level of properties: spatial level and semantic level. Thus, the mapping
relationship of verbal cues with visual target have both spatial congruency (physical location of
the cue to visual indication) and semantic congruency (meaning of the cue to visual indication).
For example, when the visual cue indicates change to the left lane, the participant will hear a
verbal cue, “LEFT” coming from the right speaker. This situation counts as semantically congruent
and spatially incongruent condition. )
10. 4.3 Apparatus & Scenario
1/18/2016 18/30The plot of a whole track ----driving trajectory ----- steering wheel angle
18/30
The scenario was developed according to ISO on the basis of OpenDSv2.5. It requires participants
to rapidly change lane as soon as they receive the signal. The measures of performance compared
with the unimodal condition reflect the multimodal perception facilitation or impairment.
It’s straight course with three lanes and 18 lane change signs. Each track is 2min long and there
are around 6 seconds between each lane-change.
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Visual
only
Unimodal
/Visual-
Only
Spaital
Congruency
78% Spatial
Incongruency
Spatial &
Semantical
Congruency
78% Spatial
Congruency
Semantic
Incongruency
78% Spatial
Incongruency
Semantic
Congruency
78% Spatial
Incongruency
Semantic
Incongruency
Track0 Synchrony Track 8 Track 2 Track 12 Track 5 Track 10 Track3
Track13 Asynchrony Track 4 Track 6 Track 1 Track 11 Track 7 Track9
Nonverbal Cue Verbal Cue
4.4 Experimental Design
• within-subjects variables: 2 timing * 3 modality * 2 congruency
The experiment is a 2 (timing) * 3 (modality) * 2 (congruency) within-subjects design. In timing
wise, I have synchrony vs. asynchrony conditions, indicating the temporal gap between audio-
visual stimuli.
In modality wise, I have visual, verbal and nonverbal cues. Each participant will perform a total of
14 tracks, consisting of two-time control conditions (visual only tracks) and twelve crossmodal
conditions. (four are nonverbal and eight are verbal)
11. 4.4 Experimental Design (cont’d): Counterbalancing
• Table 3. The exposure order of fourteen tracks
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Order A: 1st Visual Only Track – Audio/Visual Tracks (e.g., 1>2>3>4>5>6) – 2nd Visual Only Track – Audio/Visual Tracks (7>8>9>10)
Order B: 1st Visual Only Track – Audio/Visual Tracks (e.g., 10>9>8>7>6>5) – 2nd Visual Only Track – Audio/Visual Tracks (4>3>2>1)
The exposure order of 14 tracks as table 3 shows Participants will be randomly distributed into
two groups for counterbalancing purpose. This different ordering considers timing and modality
as much as possible. In this way, participants can hardly adapt to the visual and auditory cue
patterns.
4.5 Procedure
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1. Sign consent
form
2. Adjust driving
seat and watch
instruction video
3. Training track
and equivalent
hearing test
4. Start 1st baseline
(visual-only) track
5. Audio-visual
tracks
(counterbalanced
across participants)
6. 2nd baseline
(visual-only) track
in 7th, 8th,9th,10th
run
7. Complete the
remaining tracks
8. Debrief and
thank
After signing consent form, participants will watch an instruction video about an overview of the
experiment and guidance on how to use driving simulator. The video will show participants how
to quickly and efficiently change lanes when the lane change symbol appears in a training task.
Participants need to complete a training track containing all possible combinations of multimodal
signals, which might appear in the following driving task. Before the real test starts, participants
12. will adjust the seat and have a equivalent hearing test in training track. Four words (LEFT, LEF-
LEFT, RIGHT, RIGH-RIGHT) will be given through headphones at various levels of loudness. 50%
correctness is a pass for that test. The real experiment will start when the participants confirm
that they understand the whole process. A RT histogram will pop out when the participant
finishes each track.
Variable & Metrics
5.1 Criteria & Metrics
• Reaction Time (= Event detection)
• Accuracy & Error Type (= Percent of correct lane)
• Lateral Control (= Mean deviation)
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Speed
accuracy
tradeoff
5.2 Reaction Time
• Reaction Timer starts when
the first cues appear.
• Reaction Timer ends when
the car remains straight in
the targeted lane for two
seconds.
• These two seconds will be
subtracted from the total RT.
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200
msec
40 meter
Asynchronous
Audio cues
generated
Visual cues
generated
Lateral control
after completing
lane change
13. The maximum RT window for correct completion lane change is either seven seconds or 117
meter after the lane change sign, which has been defaulted in OpenDS Reaction Task settings.
5.3 Accuracy & Error Type
• “Correct LC”: the end position of the
driver is in the attended lane,
• “No LC”: the driver is in the same Li zone
at start and end positions,
• “Erroneous LC”: the end position of the
driver is in another lane than in the
attended one.
• “Loss of Control LC”: the end position of
the driver is in one of the Oi zone,
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The lateral position was measured relative to the road (and not relative to a specific lane). The
accuracy of lane-change completeness is termed as the percent correct lane (PCL). PCL is defined
by the driver’s position before and after its realization. The visible point of lane change sign is 40
meters ahead of the sign position. So, participants have 110 m to complete lane change and
maintain in the center of the targeted lane which provides a buffer if participants fail in previous
maneuver).
For each road segment between two signs, the lane where the vehicle was most frequently
positioned was identified. Consistent lane choices were then defined as those cases where the
vehicle remained in the lane for more than 75% of the segment. This selected lane was then
compared to the correct target lane. For each track, the Percent Correct Lane was then calculated
as the fraction of the consistent lane choices that were correct.
To determine this position, the 3 lane-road has been divided into different zones, corresponding
to parts of the lanes. The pink zones L1 to L3 correspond to a correct position in lane1 (left lane),
lane2 (center lane) or lane3 (right lane), while the pink zones “O” correspond to out of lane
positions (Figure 5). The lateral position of the driver is defined by the zone which contains the
75% of his/her trajectory between two signs. If not, then the position is considered as being out
of lane and the reaction timer will output an NA instead of RT. The correctness of each lane
change has been defined as follow: 1) “Correct LC”: the end position of the driver is in the
attended lane, 2) “No LC”: the driver is in the same Li zone at start and end positions, 3)
“Erroneous LC”: the end position of the driver is in another lane than in the attended one.
14. 5.4 Lateral Control
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Lane deviation calculation plot samples.
Red one is the base track. Grey is the deviated area.
Worse performance: large deviationBetter performance: small deviation
Mean deviation (Mdev) comes from the difference between the position of the condition
trajectory over the baseline curve (Simplifed with algorithm in path mapping ). With the Mdev, I
can calculate the lane-change behavior variance between the baseline run and the condition run.
Also, I can obtain the individual differences by comparing every participant’s baseline run with
the optimal curve. Outliers could be excluded if Mdev is larger than 1.2 (Tattegrain et al., 2009).
References
• Michon, J. A. (1985). A critical view of driver behavior models: what do we know, what should we
do? Human behavior and traffic safety (pp. 485-524): Springer.
• ISO, I. (2010). 26022: 2010 Road vehicles–Ergonomic aspects of transport information and control
systems–Simulated lane change test to assess in-vehicle secondary task demand. Norm.
International Organization for Standardization, Geneva, Switzerland, 24.
• Koppen, C., & Spence, C. (2007). Audiovisual asynchrony modulates the Colavita visual dominance
effect. Brain research, 1186, 224-232.
• Tattegrain, H., Bruyas, M.-P., & Karmann, N. (2009). Comparison Between Adaptive and Basic
Model Metrics in Lane Change Test to Assess In-Vehicle Secondary Task Demand. Paper presented
at the PROCEEDINGS OF THE 21ST (ESV) INTERNATIONAL TECHNICAL CONFERENCE ON THE
ENHANCED SAFETY OF VEHICLES, HELD JUNE 2009, STUTTGART, GERMANY.
• Sinnett, S., Soto-Faraco, S., & Spence, C. (2008). The co-occurrence of multisensory competition
and facilitation. Acta Psychol (Amst), 128(1), 153-161.
• Wickens, C. D., Hollands, J. G., Banbury, S., & Parasuraman, R. (2013). Engineering Psychology and
Human Performance: Pearson Education, Limited.
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Appendix Power Analysis
To compare the effect size of learning effect and condition effect, I compared the 1st visual
MeanRT and the 6th visual MeanRT. The effect size of learning effect (cohen's d) = 0.206. The
15. power to find the learning effect with 4 participant was 0.1. (small = 0. 2, medium = 0.5, large =
0.8 for paired samples t-tests power analysis)
Secondly, I wanted to run a repeated measures ANOVA based on data from the pilot study. With
the same power to find the learning effect, I need 9.1 person per group to find the condition
effect.
aovfour<-aov(reactionTime~1+condition+SpatialCongruency+Subj+SignNo.,data=four3)
Analysis of Variance Table
Response: reactionTime
Df Sum Sq Mean Sq F value Pr(>F)
condition 3 2648816 882939 2.8991 0.03569 *
SpatialCongruency 1 145476 145476 0.4777 0.49014
group 3 86612017 28870672 94.7944 < 2e-16 ***
comment 17 55891159 3287715 10.7949 < 2e-16 ***
Residuals 245 74617407 304561
Accordingly, I use Sum of Square of each factor, divided by Sum Sq total. The effect size of
condition was 0.012. Effect size of spatial congruency was 0.0006. Effect size of individual
difference was 0.394. Effect size of other noise was 0.340. Effect size comes from conditions and
spatial congruency was very small. Most variance comes from individual difference and the sign
order. Then, I use pwr package to calculate the power of condition effect is 0.05.
> pwr.anova.test(k = 4, n = 18, sig.level = 0.05, f=0.01)
Balanced one-way analysis of variance power calculation
k = 4
n = 18
f = 0.01
sig.level = 0.05
power = 0.05039755
But I think the result above is between-subjects ANOVA instead of within-subjects ANOVA. So I
used stats::power.anova.test and recalculated it. With the same power to find the learning effect,
I need 9.1 person per group to find the condition effect.
power.anova.test(groups = 4,
+ between.var = 13425.09,
+ within.var = 420396.5, power = .1)
Balanced one-way analysis of variance power calculation
groups = 4
n = 9.136422
between.var = 13425.09
within.var = 420396.5
sig.level = 0.05
power = 0.1