An intelligent multimodal interface for in-car communication systems

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 (SUIs) have identified usability issues that can affect the workload, performance, satisfaction and user experience of the driver. This is due to current speech technologies which can be a source of errors that may frustrate the driver and negatively affect the user experience. The aim of this research was to design a new multimodal interface that will manage the interaction between an ICCS and the driver. Unlike the current ICCS, it should make more voice input available, so as to support tasks (e.g. sending text messages; browsing the phone book, etc), which still require a cognitive workload from the driver. An adaptive multimodal interface was proposed in order to address current ICCS issues. The multimodal interface used both speech and manual input; however only the speech channel is used as output. This was done in order to minimise the visual distraction that graphical user interfaces or haptics devices can cause with current ICCS. The adaptive interface was designed to minimise the cognitive distraction of the driver. The adaptive interface ensures that whenever the distraction level of the driver is high, any information communication is postponed. After the design and the implementation of the first version of the prototype interface, called MIMI, a usability evaluation was conducted in order to identify any possible usability issues. Although voice dialling was found to be problematic, the results were encouraging in terms of performance, workload and user satisfaction. The suggestions received from the participants to improve the system usability were incorporated in the next implementation of MIMI. The adaptive module was then implemented to reduce driver distraction based on the driver‟s current context. The proposed architecture showed encouraging results in terms of usability and safety. The adaptive behaviour of MIMI significantly contributed to the reduction of cognitive distraction, because drivers received less information during difficult driving situations

A model for mobile, context-aware in-car communication systems to reduce driver distractions

Driver distraction remains a matter of concern throughout the world as the number of car accidents caused by distracted driving is still unacceptably high. Industry and academia are working intensively to design new techniques that will address all types of driver distraction including visual, manual, auditory and cognitive distraction. This research focuses on an existing technology, namely in-car communication systems (ICCS). ICCS allow drivers to interact with their mobile phones without touching or looking at them. Previous research suggests that ICCS have reduced visual and manual distraction. Two problems were identified in this research: existing ICCS are still expensive and only available in limited models of car. As a result of that, only a small number of drivers can obtain a car equipped with an ICCS, especially in developing countries. The second problem is that existing ICCS are not aware of the driving context, which plays a role in distracting drivers. This research project was based on the following thesis statement: A mobile, context-aware model can be designed to reduce driver distraction caused by the use of ICCS. A mobile ICCS is portable and can be used in any car, addressing the first problem. Context-awareness will be used to detect possible situations that contribute to distracting drivers and the interaction with the mobile ICCS will be adapted so as to avert calls and text messages. This will address the second problem. As the driving context is dynamic, drivers may have to deal with critical safety-related tasks while they are using an existing ICCS. The following steps were taken in order to validate the thesis statement. An investigation was conducted into the causes and consequences of driver distraction. A review of literature was conducted on context-aware techniques that could potentially be used. The design of a model was proposed, called the Multimodal Interface for Mobile Info-communication with Context (MIMIC) and a preliminary usability evaluation was conducted in order to assess the feasibility of a speech-based, mobile ICCS. Despite some problems with the speech recognition, the results were satisfying and showed that the proposed model for mobile ICCS was feasible. Experiments were conducted in order to collect data to perform supervised learning to determine the driving context. The aim was to select the most effective machine learning techniques to determine the driving context. Decision tree and instance-based algorithms were found to be the best performing algorithms. Variables such as speed, acceleration and linear acceleration were found to be the most important variables according to an analysis of the decision tree. The initial MIMIC model was updated to include several adaptation effects and the resulting model was implemented as a prototype mobile application, called MIMIC-Prototype