Patrick Tchankue | Nelson Mandela Metropolitan University (original) (raw)

Papers by Patrick Tchankue

Research paper thumbnail of Towards a Mobile, Context-Aware In-Car Communication System to Reduce Driver Distraction

Abstract-Driver distraction causes a number of car accidents around the world. Several researcher... more Abstract-Driver distraction causes a number of car accidents around the world. Several researchers have focused on understanding and providing solutions to this phenomenon. In-car communication systems (ICCSs) were introduced in order to reduce driver distraction. Most ICCSs, however, are not widely accessible as they are built-into the car. This is the reason why several mobile ICCSs have recently been introduced. The usability of existing mobile ICCSs has not, however, been extensively investigated. Driver distraction can be ...

Research paper thumbnail of Using machine learning to predict the driving context whilst driving

Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference on - SAICSIT '13, 2013

ABSTRACT This paper discusses how the driving context (driving events and distraction level) can ... more ABSTRACT This paper discusses how the driving context (driving events and distraction level) can be determined using a mobile phone equipped with several sensors. The majority of existing in-car communication systems (ICCS) available today are built-in and do not use the driving context. This creates two issues: firstly, the use of an ICCS is limited to specific cars and secondly, the driver's safety remains an issue, as the driving context is not taken into account. This paper discusses two experiments in which data are collected, trained and tested in order to create a model that predicts driving events and distraction level. A mobile, context-aware application was built using the MIMIC (Multimodal Interface for Mobile Info-communication with Context) Framework. The Inference Engine uses information from several sources, namely mobile sensors, GPS and weather information, to infer both the driving event and the distraction level. The results obtained showed that the driving events and the distraction level can be accurately predicted. The driving events were predicted using the IB1 technique with an accuracy of 92.25%. In the second experiment, the distraction level was predicted with 95.16% accuracy, using the KStar (decision tree) technique. An analysis of the decision tree showed that some variables were more important than others in predicting the driving context. These variables included the speed and direction, as well as acceleration, magnetic field and orientation.

Research paper thumbnail of Are mobile in-car communication systems feasible?

Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference on - SAICSIT '12, 2012

Abstract The issue of driver distraction remains critical despite efforts that aim to reduce its ... more Abstract The issue of driver distraction remains critical despite efforts that aim to reduce its effects. In-Car Communication Systems (ICCS) were introduced to address visual and manual distraction occurring when using a mobile phone whilst driving. ICCS running on mobile phone have increased the number of people using ICCS as they can be installed at no cost and the quality of speech recognition on mobile devices is improving. Little research, however, has been conducted to investigate usability problems with mobile ICCS. This ...

Research paper thumbnail of The impact of an adaptive user interface on reducing driver distraction

Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications - AutomotiveUI '11, 2011

ABSTRACT This paper discusses the impact of an adaptive prototype in-car communication system (IC... more ABSTRACT This paper discusses the impact of an adaptive prototype in-car communication system (ICCS), called MIMI (Multimodal Interface for Mobile Info-communication), on driver distraction. Existing ICCSs attempt to minimise the visual and manual distraction, but more research needs to be done to reduce cognitive distraction. MIMI was designed to address usability and safety issues with existing ICCSs. Few ICCSs available today consider the driver's context in the design of the user interface. An adaptive user interface (AUI) was ...

Research paper thumbnail of Designing a Mobile , Context-Aware In-Car Communication System

different phone sensors and web services to determine the context of the current driving situatio... more different phone sensors and web services to determine the context of the current driving situation. Contextual and collaborative information is used to determine the distraction level of the driver. Based on the distraction level, different adaptation effects are applied in order to reduce the driver distraction. Example adaptation effects include delaying or cancelling incoming calls and text messages and sharing the driver status with the caller. The design of MIMIC is discussed, together with its architecture.

Research paper thumbnail of Using Machine Learning to Predict the Driving Context whilst Driving

This paper discusses how the driving context (driving events and distraction level) can be determ... more This paper discusses how the driving context (driving events and distraction level) can be determined using a mobile phone equipped with several sensors. The majority of existing in-car communication systems (ICCS) available today are built-in and do not use the driving context. This creates two issues: firstly, the use of an ICCS is limited to specific cars and secondly, the driver's safety remains an issue, as the driving context is not taken into account. This paper discusses two experiments in which data are collected, trained and tested in order to create a model that predicts driving events and distraction level. A mobile, context-aware application was built using the MIMIC (Multimodal Interface for Mobile Info-communication with Context) Framework. The Inference Engine uses information from several sources, namely mobile sensors, GPS and weather information, to infer both the driving event and the distraction level. The results obtained showed that the driving events and the distraction level can be accurately predicted. The driving events were predicted using the IB1 technique with an accuracy of 92.25%. In the second experiment, the distraction level was predicted with 95.16% accuracy, using the KStar (decision tree) technique. An analysis of the decision tree showed that some variables were more important than others in predicting the driving context. These variables included the speed and direction, as well as acceleration, magnetic field and orientation.

Research paper thumbnail of An intelligent multimodal interface for In-Car Communication Systems

SUMMARY In-car communication systems (ICCS) are becoming more frequently used by drivers. ICCS ar... more SUMMARY In-car communication systems (ICCS) are becoming more frequently used by drivers. ICCS are used in order to minimise the driving distraction due to using a mobile phone while driving. Several usability studies of ICCS utilising speech user interfaces ( ...

Research paper thumbnail of The impact of an adaptive user interface on reducing driver distraction

Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications - AutomotiveUI '11, 2011

This paper discusses the impact of an adaptive prototype in-car communication system (ICCS), call... more This paper discusses the impact of an adaptive prototype in-car communication system (ICCS), called MIMI (Multimodal Interface for Mobile Info-communication), on driver distraction. Existing ICCSs attempt to minimise the visual and manual distraction, but more research needs to be done to reduce cognitive distraction. MIMI was designed to address usability and safety issues with existing ICCSs. Few ICCSs available today consider the driver's context in the design of the user interface. An adaptive user interface (AUI) was designed and integrated into a conventional dialogue system in order to prevent the driver from receiving calls and sending text messages under high distraction conditions. The current distraction level is detected by a neural network using the driving speed and steering wheel angle of the car as inputs. An adaptive version of MIMI was compared to a non-adaptive version in a user study conducted using a simple driving simulator. The results obtained showed that the adaptive version provided several usability and safety benefits, including reducing the cognitive load, and that the users preferred the adaptive version.

Research paper thumbnail of MIMI: A Multimodal and Adaptive Interface for an In-Car Communication System

The number of cars provided with in-car communication systems has increased noticeably. Research ... more The number of cars provided with in-car communication systems has increased noticeably. Research has shown that using a mobile phone whilst driving has usability and safety issues. In order to minimise these issues, user interfaces that promote the usage of the hands and eyes solely for the driving task have been proposed. This paper discusses the design of MIMI (Multimodal Interface for Mobile Infocommunication), a prototype multimodal in-car communication system using a speech interface, supplemented with steering wheel button input. The design of MIMI is discussed, together with its architecture, including the dialogue manager, the adaptive module and the workload manager. The adaptive module contains a workload manager that helps MIMI to present information to the driver such that driver distraction is reduced.

Research paper thumbnail of Using machine learning to predict the driving context whilst driving

Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference on - SAICSIT '13, 2013

This paper discusses how the driving context (driving events and distraction level) can be determ... more This paper discusses how the driving context (driving events and distraction level) can be determined using a mobile phone equipped with several sensors. The majority of existing in-car communication systems (ICCS) available today are built-in and do not use the driving context. This creates two issues: firstly, the use of an ICCS is limited to specific cars and secondly, the driver's safety remains an issue, as the driving context is not taken into account. This paper discusses two experiments in which data are collected, trained and tested in order to create a model that predicts driving events and distraction level. A mobile, context-aware application was built using the MIMIC (Multimodal Interface for Mobile Info-communication with Context) Framework. The Inference Engine uses information from several sources, namely mobile sensors, GPS and weather information, to infer both the driving event and the distraction level. The results obtained showed that the driving events and the distraction level can be accurately predicted. The driving events were predicted using the IB1 technique with an accuracy of 92.25%. In the second experiment, the distraction level was predicted with 95.16% accuracy, using the KStar (decision tree) technique. An analysis of the decision tree showed that some variables were more important than others in predicting the driving context. These variables included the speed and direction, as well as acceleration, magnetic field and orientation.

Research paper thumbnail of Using Machine Learning to Predict the Driving Context whilst Driving

This paper discusses how the driving context (driving events and distraction level) can be determ... more This paper discusses how the driving context (driving events and distraction level) can be determined using a mobile phone equipped with several sensors. The majority of existing in-car communication systems (ICCS) available today are built-in and do not use the driving context. This creates two issues: firstly, the use of an ICCS is limited to specific cars and secondly, the driver's safety remains an issue, as the driving context is not taken into account. This paper discusses two experiments in which data are collected, trained and tested in order to create a model that predicts driving events and distraction level. A mobile, context-aware application was built using the MIMIC (Multimodal Interface for Mobile Info-communication with Context) Framework. The Inference Engine uses information from several sources, namely mobile sensors, GPS and weather information, to infer both the driving event and the distraction level. The results obtained showed that the driving events and the distraction level can be accurately predicted. The driving events were predicted using the IB1 technique with an accuracy of 92.25%. In the second experiment, the distraction level was predicted with 95.16% accuracy, using the KStar (decision tree) technique. An analysis of the decision tree showed that some variables were more important than others in predicting the driving context. These variables included the speed and direction, as well as acceleration, magnetic field and orientation.

Research paper thumbnail of Using Machine Learning to Predict the Driving Context whilst Driving

Research paper thumbnail of Are Mobile In-Car Communication Systems Feasible?  A Usability Study

SAICSIT, Oct 2012

The issue of driver distraction remains critical despite efforts that aim to reduce its effects. ... more The issue of driver distraction remains critical despite efforts that aim to reduce its effects. In-Car Communication Systems (ICCS) were introduced to address visual and manual distraction occurring when using a mobile phone whilst driving. ICCS running on mobile phone have increased the number of people using ICCS as they can be installed at no cost and the quality of speech recognition on mobile devices is improving. Little research, however, has been conducted to investigate usability problems with mobile ICCS.

Research paper thumbnail of Designing a Mobile, Context-Aware In-Car Communication System

Research paper thumbnail of Towards a Mobile, Context-Aware In-Car Communication System to Reduce Driver Distraction

satnac.org.za

Driver distraction causes a number of car accidents around the world. Several researchers have fo... more Driver distraction causes a number of car accidents around the world. Several researchers have focused on understanding and providing solutions to this phenomenon. In-car communication systems (ICCSs) were introduced in order to reduce driver distraction. Most ICCSs, however, are not widely accessible as they are built-into the car. This is the reason why several mobile ICCSs have recently been introduced. The usability of existing mobile ICCSs has not, however, been extensively investigated. Driver distraction can be caused by external conditions (e.g. driving, weather and road conditions), but most ICCSs do not take these conditions into account. This paper presents an investigation into a mobile, context-aware ICCS in order to develop a model that can be used to reduce driver distraction.

Research paper thumbnail of The Impact of an Adaptive User Interface on Reducing Driver Distraction

This paper discusses the impact of an adaptive prototype in-car communication system (ICCS), ... more This paper discusses the impact of an adaptive prototype in-car
communication system (ICCS), called MIMI (Multimodal
Interface for Mobile Info-communication), on driver distraction.
Existing ICCSs attempt to minimise the visual and manual
distraction, but more research needs to be done to reduce
cognitive distraction. MIMI was designed to address usability and
safety issues with existing ICCSs. Few ICCSs available today
consider the driver’s context in the design of the user interface.
An adaptive user interface (AUI) was designed and integrated into
a conventional dialogue system in order to prevent the driver from
receiving calls and sending text messages under high distraction
conditions. The current distraction level is detected by a neural
network using the driving speed and steering wheel angle of the
car as inputs. An adaptive version of MIMI was compared to a
non-adaptive version in a user study conducted using a simple
driving simulator. The results obtained showed that the adaptive
version provided several usability and safety benefits, including
reducing the cognitive load, and that the users preferred the
adaptive version.

Research paper thumbnail of An Intelligent Multimodal Interface for In-Car Communication Systems

Research paper thumbnail of MIMI: A Multimodal and Adaptive Interface for an In-Car Communication System

satnac.org.za

The number of cars provided with in-car communication systems has increased noticeably. Research ... more The number of cars provided with in-car communication systems has increased noticeably. Research has shown that using a mobile phone whilst driving has usability and safety issues. In order to minimise these issues, user interfaces that promote the usage of the hands and eyes solely for the driving task have been proposed. This paper discusses the design of MIMI (Multimodal Interface for Mobile Infocommunication), a prototype multimodal in-car communication system using a speech interface, supplemented with steering wheel button input. The design of MIMI is discussed, together with its architecture, including the dialogue manager, the adaptive module and the workload manager. The adaptive module contains a workload manager that helps MIMI to present information to the driver such that driver distraction is reduced.

Research paper thumbnail of Design and evaluation of a multimodal interface for in-car communication systems

… of the South African Institute of …, Jan 1, 2010

Eliciting and analy zing requi rements within knowledge systems, which fundamentally differ so fa... more Eliciting and analy zing requi rements within knowledge systems, which fundamentally differ so far from technology supported systems represent particular challenges. African rural communities' life is deeply root ed in an African Indigenous knowledge sy stem manifested in their practices such as Traditional Medicine. We descri be our endeavors to elicit requirements to design a sy stem to support the accumulation and sharing of traditional local knowledge within two rural Herero communities in Nam ibia. We show how our m ethod addressed various challenges in eliciting and depicting intangible principles arising because African com munities do not dichotomize theoretical and practical know-how or privilege a science of abstraction and generalization. Ethnography provided insights into etiology , or causal interrelationships between s ocial values , s piritual elements and everyday life. Participatory methods, involving youth and elders, revealed nuances in social relations and pedagogy pertinent to the transfer of knowledge from generation to generation. Researcher and participant-recorded audio-visual media revealed that interactions prioritize s peech, gesture and bodily interaction, above visual context. Analy sis of the performed and narrated structures reveal some of the way s that people tacitly transfer bodily and felt-experiences and temporal patterns in story telling. Experiments using digital and paperbased media, in situ rurally showed the ways that people in rural settings encounter and learn within their everyday experiences of the land. Thes e analy ses als o demonstrate that own ontological and representational bias es can cons train eliciting local meanings and analy zing transformations in meaning as we introduce media. Reflections on our method are of value to others who need to elicit requirements in communities whose literacy , social and spiritual logic and values profoundly differ from those in the knowledge sy stems that typify ICT design.

Research paper thumbnail of Towards a Multimodal Interface for In-Car Communication Systems

satnac.org.za

The number of cars provided with an in-car communication system has considerably increased during... more The number of cars provided with an in-car communication system has considerably increased during the past few years. Using a mobile phone whilst driving is a safety-critical task and can cause usability issues. Speech modality has been incorporated in order to allocate hands and eyes solely to the driving task speech. This paper discusses an investigation into in-car communication systems and multimodal interfaces in order to design an in-car communication system which improves the driving experience and meets usability goals. This multimodal interface will make use of speech and manual control as input and output modalities.

Research paper thumbnail of Towards a Mobile, Context-Aware In-Car Communication System to Reduce Driver Distraction

Abstract-Driver distraction causes a number of car accidents around the world. Several researcher... more Abstract-Driver distraction causes a number of car accidents around the world. Several researchers have focused on understanding and providing solutions to this phenomenon. In-car communication systems (ICCSs) were introduced in order to reduce driver distraction. Most ICCSs, however, are not widely accessible as they are built-into the car. This is the reason why several mobile ICCSs have recently been introduced. The usability of existing mobile ICCSs has not, however, been extensively investigated. Driver distraction can be ...

Research paper thumbnail of Using machine learning to predict the driving context whilst driving

Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference on - SAICSIT '13, 2013

ABSTRACT This paper discusses how the driving context (driving events and distraction level) can ... more ABSTRACT This paper discusses how the driving context (driving events and distraction level) can be determined using a mobile phone equipped with several sensors. The majority of existing in-car communication systems (ICCS) available today are built-in and do not use the driving context. This creates two issues: firstly, the use of an ICCS is limited to specific cars and secondly, the driver's safety remains an issue, as the driving context is not taken into account. This paper discusses two experiments in which data are collected, trained and tested in order to create a model that predicts driving events and distraction level. A mobile, context-aware application was built using the MIMIC (Multimodal Interface for Mobile Info-communication with Context) Framework. The Inference Engine uses information from several sources, namely mobile sensors, GPS and weather information, to infer both the driving event and the distraction level. The results obtained showed that the driving events and the distraction level can be accurately predicted. The driving events were predicted using the IB1 technique with an accuracy of 92.25%. In the second experiment, the distraction level was predicted with 95.16% accuracy, using the KStar (decision tree) technique. An analysis of the decision tree showed that some variables were more important than others in predicting the driving context. These variables included the speed and direction, as well as acceleration, magnetic field and orientation.

Research paper thumbnail of Are mobile in-car communication systems feasible?

Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference on - SAICSIT '12, 2012

Abstract The issue of driver distraction remains critical despite efforts that aim to reduce its ... more Abstract The issue of driver distraction remains critical despite efforts that aim to reduce its effects. In-Car Communication Systems (ICCS) were introduced to address visual and manual distraction occurring when using a mobile phone whilst driving. ICCS running on mobile phone have increased the number of people using ICCS as they can be installed at no cost and the quality of speech recognition on mobile devices is improving. Little research, however, has been conducted to investigate usability problems with mobile ICCS. This ...

Research paper thumbnail of The impact of an adaptive user interface on reducing driver distraction

Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications - AutomotiveUI '11, 2011

ABSTRACT This paper discusses the impact of an adaptive prototype in-car communication system (IC... more ABSTRACT This paper discusses the impact of an adaptive prototype in-car communication system (ICCS), called MIMI (Multimodal Interface for Mobile Info-communication), on driver distraction. Existing ICCSs attempt to minimise the visual and manual distraction, but more research needs to be done to reduce cognitive distraction. MIMI was designed to address usability and safety issues with existing ICCSs. Few ICCSs available today consider the driver's context in the design of the user interface. An adaptive user interface (AUI) was ...

Research paper thumbnail of Designing a Mobile , Context-Aware In-Car Communication System

different phone sensors and web services to determine the context of the current driving situatio... more different phone sensors and web services to determine the context of the current driving situation. Contextual and collaborative information is used to determine the distraction level of the driver. Based on the distraction level, different adaptation effects are applied in order to reduce the driver distraction. Example adaptation effects include delaying or cancelling incoming calls and text messages and sharing the driver status with the caller. The design of MIMIC is discussed, together with its architecture.

Research paper thumbnail of Using Machine Learning to Predict the Driving Context whilst Driving

This paper discusses how the driving context (driving events and distraction level) can be determ... more This paper discusses how the driving context (driving events and distraction level) can be determined using a mobile phone equipped with several sensors. The majority of existing in-car communication systems (ICCS) available today are built-in and do not use the driving context. This creates two issues: firstly, the use of an ICCS is limited to specific cars and secondly, the driver's safety remains an issue, as the driving context is not taken into account. This paper discusses two experiments in which data are collected, trained and tested in order to create a model that predicts driving events and distraction level. A mobile, context-aware application was built using the MIMIC (Multimodal Interface for Mobile Info-communication with Context) Framework. The Inference Engine uses information from several sources, namely mobile sensors, GPS and weather information, to infer both the driving event and the distraction level. The results obtained showed that the driving events and the distraction level can be accurately predicted. The driving events were predicted using the IB1 technique with an accuracy of 92.25%. In the second experiment, the distraction level was predicted with 95.16% accuracy, using the KStar (decision tree) technique. An analysis of the decision tree showed that some variables were more important than others in predicting the driving context. These variables included the speed and direction, as well as acceleration, magnetic field and orientation.

Research paper thumbnail of An intelligent multimodal interface for In-Car Communication Systems

SUMMARY In-car communication systems (ICCS) are becoming more frequently used by drivers. ICCS ar... more SUMMARY In-car communication systems (ICCS) are becoming more frequently used by drivers. ICCS are used in order to minimise the driving distraction due to using a mobile phone while driving. Several usability studies of ICCS utilising speech user interfaces ( ...

Research paper thumbnail of The impact of an adaptive user interface on reducing driver distraction

Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications - AutomotiveUI '11, 2011

This paper discusses the impact of an adaptive prototype in-car communication system (ICCS), call... more This paper discusses the impact of an adaptive prototype in-car communication system (ICCS), called MIMI (Multimodal Interface for Mobile Info-communication), on driver distraction. Existing ICCSs attempt to minimise the visual and manual distraction, but more research needs to be done to reduce cognitive distraction. MIMI was designed to address usability and safety issues with existing ICCSs. Few ICCSs available today consider the driver's context in the design of the user interface. An adaptive user interface (AUI) was designed and integrated into a conventional dialogue system in order to prevent the driver from receiving calls and sending text messages under high distraction conditions. The current distraction level is detected by a neural network using the driving speed and steering wheel angle of the car as inputs. An adaptive version of MIMI was compared to a non-adaptive version in a user study conducted using a simple driving simulator. The results obtained showed that the adaptive version provided several usability and safety benefits, including reducing the cognitive load, and that the users preferred the adaptive version.

Research paper thumbnail of MIMI: A Multimodal and Adaptive Interface for an In-Car Communication System

The number of cars provided with in-car communication systems has increased noticeably. Research ... more The number of cars provided with in-car communication systems has increased noticeably. Research has shown that using a mobile phone whilst driving has usability and safety issues. In order to minimise these issues, user interfaces that promote the usage of the hands and eyes solely for the driving task have been proposed. This paper discusses the design of MIMI (Multimodal Interface for Mobile Infocommunication), a prototype multimodal in-car communication system using a speech interface, supplemented with steering wheel button input. The design of MIMI is discussed, together with its architecture, including the dialogue manager, the adaptive module and the workload manager. The adaptive module contains a workload manager that helps MIMI to present information to the driver such that driver distraction is reduced.

Research paper thumbnail of Using machine learning to predict the driving context whilst driving

Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference on - SAICSIT '13, 2013

This paper discusses how the driving context (driving events and distraction level) can be determ... more This paper discusses how the driving context (driving events and distraction level) can be determined using a mobile phone equipped with several sensors. The majority of existing in-car communication systems (ICCS) available today are built-in and do not use the driving context. This creates two issues: firstly, the use of an ICCS is limited to specific cars and secondly, the driver's safety remains an issue, as the driving context is not taken into account. This paper discusses two experiments in which data are collected, trained and tested in order to create a model that predicts driving events and distraction level. A mobile, context-aware application was built using the MIMIC (Multimodal Interface for Mobile Info-communication with Context) Framework. The Inference Engine uses information from several sources, namely mobile sensors, GPS and weather information, to infer both the driving event and the distraction level. The results obtained showed that the driving events and the distraction level can be accurately predicted. The driving events were predicted using the IB1 technique with an accuracy of 92.25%. In the second experiment, the distraction level was predicted with 95.16% accuracy, using the KStar (decision tree) technique. An analysis of the decision tree showed that some variables were more important than others in predicting the driving context. These variables included the speed and direction, as well as acceleration, magnetic field and orientation.

Research paper thumbnail of Using Machine Learning to Predict the Driving Context whilst Driving

This paper discusses how the driving context (driving events and distraction level) can be determ... more This paper discusses how the driving context (driving events and distraction level) can be determined using a mobile phone equipped with several sensors. The majority of existing in-car communication systems (ICCS) available today are built-in and do not use the driving context. This creates two issues: firstly, the use of an ICCS is limited to specific cars and secondly, the driver's safety remains an issue, as the driving context is not taken into account. This paper discusses two experiments in which data are collected, trained and tested in order to create a model that predicts driving events and distraction level. A mobile, context-aware application was built using the MIMIC (Multimodal Interface for Mobile Info-communication with Context) Framework. The Inference Engine uses information from several sources, namely mobile sensors, GPS and weather information, to infer both the driving event and the distraction level. The results obtained showed that the driving events and the distraction level can be accurately predicted. The driving events were predicted using the IB1 technique with an accuracy of 92.25%. In the second experiment, the distraction level was predicted with 95.16% accuracy, using the KStar (decision tree) technique. An analysis of the decision tree showed that some variables were more important than others in predicting the driving context. These variables included the speed and direction, as well as acceleration, magnetic field and orientation.

Research paper thumbnail of Using Machine Learning to Predict the Driving Context whilst Driving

Research paper thumbnail of Are Mobile In-Car Communication Systems Feasible?  A Usability Study

SAICSIT, Oct 2012

The issue of driver distraction remains critical despite efforts that aim to reduce its effects. ... more The issue of driver distraction remains critical despite efforts that aim to reduce its effects. In-Car Communication Systems (ICCS) were introduced to address visual and manual distraction occurring when using a mobile phone whilst driving. ICCS running on mobile phone have increased the number of people using ICCS as they can be installed at no cost and the quality of speech recognition on mobile devices is improving. Little research, however, has been conducted to investigate usability problems with mobile ICCS.

Research paper thumbnail of Designing a Mobile, Context-Aware In-Car Communication System

Research paper thumbnail of Towards a Mobile, Context-Aware In-Car Communication System to Reduce Driver Distraction

satnac.org.za

Driver distraction causes a number of car accidents around the world. Several researchers have fo... more Driver distraction causes a number of car accidents around the world. Several researchers have focused on understanding and providing solutions to this phenomenon. In-car communication systems (ICCSs) were introduced in order to reduce driver distraction. Most ICCSs, however, are not widely accessible as they are built-into the car. This is the reason why several mobile ICCSs have recently been introduced. The usability of existing mobile ICCSs has not, however, been extensively investigated. Driver distraction can be caused by external conditions (e.g. driving, weather and road conditions), but most ICCSs do not take these conditions into account. This paper presents an investigation into a mobile, context-aware ICCS in order to develop a model that can be used to reduce driver distraction.

Research paper thumbnail of The Impact of an Adaptive User Interface on Reducing Driver Distraction

This paper discusses the impact of an adaptive prototype in-car communication system (ICCS), ... more This paper discusses the impact of an adaptive prototype in-car
communication system (ICCS), called MIMI (Multimodal
Interface for Mobile Info-communication), on driver distraction.
Existing ICCSs attempt to minimise the visual and manual
distraction, but more research needs to be done to reduce
cognitive distraction. MIMI was designed to address usability and
safety issues with existing ICCSs. Few ICCSs available today
consider the driver’s context in the design of the user interface.
An adaptive user interface (AUI) was designed and integrated into
a conventional dialogue system in order to prevent the driver from
receiving calls and sending text messages under high distraction
conditions. The current distraction level is detected by a neural
network using the driving speed and steering wheel angle of the
car as inputs. An adaptive version of MIMI was compared to a
non-adaptive version in a user study conducted using a simple
driving simulator. The results obtained showed that the adaptive
version provided several usability and safety benefits, including
reducing the cognitive load, and that the users preferred the
adaptive version.

Research paper thumbnail of An Intelligent Multimodal Interface for In-Car Communication Systems

Research paper thumbnail of MIMI: A Multimodal and Adaptive Interface for an In-Car Communication System

satnac.org.za

The number of cars provided with in-car communication systems has increased noticeably. Research ... more The number of cars provided with in-car communication systems has increased noticeably. Research has shown that using a mobile phone whilst driving has usability and safety issues. In order to minimise these issues, user interfaces that promote the usage of the hands and eyes solely for the driving task have been proposed. This paper discusses the design of MIMI (Multimodal Interface for Mobile Infocommunication), a prototype multimodal in-car communication system using a speech interface, supplemented with steering wheel button input. The design of MIMI is discussed, together with its architecture, including the dialogue manager, the adaptive module and the workload manager. The adaptive module contains a workload manager that helps MIMI to present information to the driver such that driver distraction is reduced.

Research paper thumbnail of Design and evaluation of a multimodal interface for in-car communication systems

… of the South African Institute of …, Jan 1, 2010

Eliciting and analy zing requi rements within knowledge systems, which fundamentally differ so fa... more Eliciting and analy zing requi rements within knowledge systems, which fundamentally differ so far from technology supported systems represent particular challenges. African rural communities' life is deeply root ed in an African Indigenous knowledge sy stem manifested in their practices such as Traditional Medicine. We descri be our endeavors to elicit requirements to design a sy stem to support the accumulation and sharing of traditional local knowledge within two rural Herero communities in Nam ibia. We show how our m ethod addressed various challenges in eliciting and depicting intangible principles arising because African com munities do not dichotomize theoretical and practical know-how or privilege a science of abstraction and generalization. Ethnography provided insights into etiology , or causal interrelationships between s ocial values , s piritual elements and everyday life. Participatory methods, involving youth and elders, revealed nuances in social relations and pedagogy pertinent to the transfer of knowledge from generation to generation. Researcher and participant-recorded audio-visual media revealed that interactions prioritize s peech, gesture and bodily interaction, above visual context. Analy sis of the performed and narrated structures reveal some of the way s that people tacitly transfer bodily and felt-experiences and temporal patterns in story telling. Experiments using digital and paperbased media, in situ rurally showed the ways that people in rural settings encounter and learn within their everyday experiences of the land. Thes e analy ses als o demonstrate that own ontological and representational bias es can cons train eliciting local meanings and analy zing transformations in meaning as we introduce media. Reflections on our method are of value to others who need to elicit requirements in communities whose literacy , social and spiritual logic and values profoundly differ from those in the knowledge sy stems that typify ICT design.

Research paper thumbnail of Towards a Multimodal Interface for In-Car Communication Systems

satnac.org.za

The number of cars provided with an in-car communication system has considerably increased during... more The number of cars provided with an in-car communication system has considerably increased during the past few years. Using a mobile phone whilst driving is a safety-critical task and can cause usability issues. Speech modality has been incorporated in order to allocate hands and eyes solely to the driving task speech. This paper discusses an investigation into in-car communication systems and multimodal interfaces in order to design an in-car communication system which improves the driving experience and meets usability goals. This multimodal interface will make use of speech and manual control as input and output modalities.