Voice Information System Adapted to Driver's Mental Workload

Abstract

There is a risk that voice messages from in-vehicle information systems may cause a driver to be distracted while driving. To avoid such a risk the message systems need to be adapted to drivers' mental workload. Such adaptive systems deliver voice messages when drivers' mental workload is low and postpone the messages when the driver workload is high. It is important for the system to estimate the current driver workload from car sensors such as car speed, steering wheel angle, accelerator pedal position and so on. In order to find some relations between the driver's mental workload and the data from car sensors, a dual task experiment was conducted on a public road. In the experiment, participants performed a memory task while driving an experimental car. At the same time, the data from the car sensors were recorded. The correlation coefficients between the memory task performance and the data from car sensors showed that release of the accelerator pedal was the most significant indicator of workload. Based on these results a workload estimator was developed, which has been applied to a voice information delivery test system. The potential of a voice information system that adapts to the driver's mental workload was evaluated.

An Eye for the Air Traffic Controller Workload

“…By using an adaptive man-machine interface that filters information presentation to drivers, Piechulla et al (2003) created a real-time workload estimator that alerts the driver based on the current workload and traffic situation. Other researchers have also developed similar designs using voice information to detect driver workload (Uchiyama et al 2004;Wood et al 2004).…”

Measurement of workload for voice user interface systems

“…In a related experiment, Uchiyama and colleagues [10] made an attempt to quantify the driver's capacity using a dual-task method. They developed a voice interface system which adapts to the driver's situation, estimating the driver's workload capacity using driving conditions.…”

A spoken dialog system with redundant response to prevent user misunderstanding

“…The SDSs should not prevent drivers' attentions by its response, especially, if the drivers' workload levels are high. There are various methods for estimating a driver's workload, for example, using a large number of automotive sensors [2], [3] and speech-related features [4]. However, there are fewer researches into the strategies using the drivers' workload levels.…”