Developing conditionally automated driving systems is on the rise. Vehicles with full longitudinal and latitudinal control will allow drivers to engage in secondary tasks without monitoring the roadway, but users may be required to resume vehicle control to handle critical hazards. The loss of driver's situational awareness increases the potential for accidents. Thus, the automated systems need to estimate the driver's ability to resume control of the driving task. The aim of this study was to assess the physiological behaviour (heart rate and pupil diameter) of drivers. The assessment was performed during two naturalistic secondary tasks. The tasks were the email and the twenty questions task in addition to a control group that did not perform any tasks. The study aimed at finding possible correlations between the driver's physiological data and their responses to a takeover request. A driving simulator study was used to collect data from a total of 33 participants in a repeated measures design to examine the physiological changes during driving and to measure their takeover quality and response time. Secondary tasks induced changes on physiological measures and a small influence on response time. However, there was a strong observed correlation between the physiological measures and response time. Takeover quality in this study was assessed using two new performance measures called PerSpeed and PerAngle. They are identified as the mean percentage change of vehicle's speed and heading angle starting from a takeover request time. Using linear mixed models, there was a strong interaction between task, heart rate and pupil diameter and PerSpeed, PerAngle and response time. This, in turn, provided a measurable understanding of a driver's future responses to the automated system based on the driver's physiological changes to allow better decision making. The present findings of this study emphasised the possibility of building a driver mental state model and prediction system to determine the quality of the driver's responses in a highly automated vehicle. Such results will reduce accidents and enhance the driver's experience in highly automated vehicles.

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GPS-Enabled devices such as smartphones are widely used. With GPS chips that uses lower energy, GPS location and traces are widely collected by different users. One important useful usage is extracting the speed of a user in vehicle to determine real-time and historical speed of traffic in each road in the city. Accumulating these speed records allows the creation of speed profiles. Speed profiles define the general behavior (i.e., traffic speed) of each road at each time of the day. In this demo, we show an energy-efficient location acquisition algorithm to collect traces, a map-matching algorithm to construct the path that the user has taken, an algorithm to calculate the average speed of the vehicle and a speed profile of different road segments used to provide historical, real-time and predicted traffic speed. We also display the whole system in action with data of over 1000 traces collected in Trento, Italy.

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