With in-vehicle information systems, there is a danger of voice messages causing the user to be distracted while driving. To reduce this danger, the ideal would be for the system to adapt to the driver's mental workload. Such an adaptive system would deliver voice messages only when the driver's mental workload was low, and suppress messages whenever his or her workload is high. Therefore, such a system would have to be able to estimate the current driver workload from the outputs of the car's sensors such as the speed, steering wheel angle, and accelerator pedal position. To establish a relationship between the driver's mental workload and the data that is output by the car's sensors, a dual-task experiment was conducted on a public road. In this experiment, participants performed a memory-task while driving a test car. At the same time, the data from the car's sensors was recorded. The correlation coefficients linking the performance of the memory-task to the data received from the car's sensors showed that the driver's releasing the accelerator pedal was the most significant indicator of workload. Based on these results, a workload estimation model was developed, which was then applied to a voice information prototype system in a test car. The driving situations in which the system postpones the delivery of voice messages were then confirmed.