Cross-correlation based performance measures for characterizing the influence of in-vehicle interfaces on driving and cognitive workload

Driving is a cognitively loading task which requires drivers\u27 full attention and coordination of both mind and body. However, drivers often engage in side activities which can negatively impact safety. A typical approach for analyzing the influences of side activities on driving is to conduct experiments in which various driving performance measures are collected, such as steering wheel angle and lane position. Those measures are then transformed, typically using means and variances, before being analyzed statistically. However, the problem is that those transformations perform averaging of the acquired data, which can result in missing short, but important events (such as glances directed off-road). As a consequence, statistically significant differences may not be observed between the tested conditions. Nevertheless, just because the influences of in-vehicle interactions do not show in the averages, it does not mean that they do not exist or should be neglected, especially if the nature of the interactions is such that they can be performed frequently (for example, with an infotainment system). This can create a false conclusion about the lack of influence of the tested side activity on driving. The main contribution of this research is in developing two new performance measures inspired by the mathematical function of cross-correlation: one which evaluates the cumulative effect and the other which evaluates the effects of individual instances of in-vehicle interactions on driving and cognitive load. The results from three driving simulator studies demonstrate that our cumulative measure provides more sensitivity to the effects of in-vehicle interactions, even when they are not detected through average-based measures. Additionally, our instance-based measure provides a low-level insight into the nature of the influence of individual in-vehicle interactions. Both measures produce results that can be ranked, which allows determining the relative size of the effect that various in-vehicle interactions have on driving. Finally, we demonstrate a set of variables which can be used for predicting the cumulative and instance-based results. This predictive ability is important, because it may allow obtaining quick simulation results without performing actual experiments, which can be used in the early stages of an interface or experiment design process

Comparing the Influence of Two User Interfaces for Mobile Radios on Driving Performance

Mobile radios have a manufacturer-provided manual user interface that allows changing radio channels using buttons. There is also a display on the faceplate of the radio that is used to visually verify channel selection. The objective of this study was to compare the influence of the manual user interface and the Project54 speech user interface (SUI) on drivers’ performance while interacting with a mobile radio. In experiments conducted with a driving simulator we found that operating the manual user interface degraded driving performance significantly, while using the SUI did not

Investigating HUDs for the Presentation of Choice Lists in Car Navigation Systems

It has been established that head-down displays (HDDs), such as those commonly placed in the dashboard of commercial automobiles, can draw drivers’ attention away from the primary driving task (Bach et al., 2008). This problem can be exacerbated when screens are “busy” with graphics or rich information. In this paper, we present the results of a driving simulator-based user study where we examined two potential alternatives to HDDs for presenting textual lists. Subjects conducted a series of street name finding tasks using each of three system variants: one with a HDD, one with a head-up display (HUD), and one with only an auditory display. We found that the auditory display had the least impact on mental load, but at the expense of task completion efficiency. The HUD variant also had a low impact on mental load and scored highest in user satisfaction, and therefore appears to be the most viable target for future study

Investigating HUDs for the Presentation of Choice Lists in Car Navigation Systems

It has been established that head-down displays (HDDs), such as those commonly placed in the dashboard of commercial automobiles, can draw drivers’ attention away from the primary driving task (Bach et al., 2008). This problem can be exacerbated when screens are “busy” with graphics or rich information. In this paper, we present the results of a driving simulator-based user study where we examined two potential alternatives to HDDs for presenting textual lists. Subjects conducted a series of street name finding tasks using each of three system variants: one with a HDD, one with a head-up display (HUD), and one with only an auditory display. We found that the auditory display had the least impact on mental load, but at the expense of task completion efficiency. The HUD variant also had a low impact on mental load and scored highest in user satisfaction, and therefore appears to be the most viable target for future study

The Effect of Speech Interface Accuracy on Driving Performance," Interspeech 2007

With the proliferation of cell phones around the world, governments have been enacting legislation prohibiting the use of cell phones during driving without a “hands-free ” kit, bringing automotive speech recognition to the forefront of public safety. At the same time, the trend in cell phone hardware has been to create smaller and thinner devices with greater computational power and functional complexity, making speech the most viable modality for user input. Given the important role that automotive speech recognition is likely to play in consumer lives, we explore how the accuracy of the speech engine, the use of the push-to-talk button, and the type of dialog repair employed by the interface influences driving performance. In experiments conducted with a driving simulator, we found that the accuracy of the speech engine and its interaction with the use of the push-to-talk button does impact driving performance significantly, but the type of dialog repair employed does not. We discuss the implications of these findings on the design of automotive speech recognition systems. Index Terms: automotive speech recognition 1