Shi Cao

Texting while driving: Is speech-based text entry less risky than handheld text entry?

Research indicates that using a cell phone to talk or text while maneuvering a vehicle impairs driving performance. However, few published studies directly compare the distracting effects of texting using a hands-free (i.e., speech-based interface) versus handheld cell phone, which is an important issue for legislation, automotive interface design and driving safety training. This study compared the effect of speech-based versus handheld text entries on simulated driving performance by asking participants to perform a car following task while controlling the duration of a secondary text-entry task. Results showed that both speech-based and handheld text entries impaired driving performance relative to the drive-only condition by causing more variation in speed and lane position. Handheld text entry also increased the brake response time and increased variation in headway distance. Text entry using a speech-based cell phone was less detrimental to driving performance than handheld text entry. Nevertheless, the speech-based text entry task still significantly impaired driving compared to the drive-only condition. These results suggest that speech-based text entry disrupts driving, but reduces the level of performance interference compared to text entry with a handheld device. In addition, the difference in the distraction effect caused by speech-based and handheld text entry is not simply due to the difference in task duration.

Concurrent processing of vehicle lane keeping and speech comprehension tasks

With the growing prevalence of using in-vehicle devices and mobile devices while driving, a major concern is their impact on driving performance and safety. However, the effects of cognitive load such as conversation on driving performance are still controversial and not well understood. In this study, an experiment was conducted to investigate the concurrent performance of vehicle lane keeping and speech comprehension tasks with improved experimental control of the confounding factors identified in previous studies. The results showed that the standard deviation of lane position (SDLP) was increased when the driving speed was faster (0.30 m at 36 km/h; 0.36 m at 72 km/h). The concurrent comprehension task had no significant effect on SDLP (0.34 m on average) or the standard deviation of steering wheel angle (SDSWA; 5.20° on average). The correct rate of the comprehension task was reduced in the dual-task condition (from 93.4% to 91.3%) compared with the comprehension single-task condition. Mental workload was significantly higher in the dual-task condition compared with the single-task conditions. Implications for driving safety were discussed.

Texting while driving: is speech-based texting less risky than handheld texting?

Research indicates that using a cell phone to talk or text while maneuvering a vehicle impairs driving performance. However, few published studies directly compare the distracting effects of texting using a hands-free (i.e., speech-based interface) versus handheld cell phone, which is an important issue for legislation, automotive interface design and driving safety training. This study compared the effect of speech-based versus handheld texting on simulated driving performance by asking participants to perform a car following task while controlling the duration of a secondary texting task. Results showed that both speech-based and handheld texting impaired driving performance relative to the drive-only condition by causing more variation in speed and lane position. Handheld texting also increased the brake response time and increased variation in headway distance. Texting using a speech-based cell phone was less detrimental to driving performance than handheld texting. Nevertheless, the speech-based texting task still significantly impaired driving compared to the drive-only condition. These results suggest that speech-based interaction disrupts driving, but reduces the levels of performance interference compared to handheld devices. In addition, the difference in the distraction effect caused by speech-based and handheld texting is not simply due to the difference in task duration.

Mental Workload Modeling in an Integrated Cognitive Architecture

Mental workload analysis is an important component in the test and evaluation of humanmachine systems. Existing empirical workload measures have limited applicability when humanin-the-loop tests are impractical, which produces the need for theory-based workload modeling and prediction methods. ACTR-QN is a theory-based integrated cognitive architecture combining the advantages of Adaptive Control of Thought-Rational (ACT-R) and Queueing Network (QN) architectures. The research reported in this paper proposes and examines a theory and method for modeling and visualizing mental workload in ACTR-QN. Validation with an empirical study of a semantic judgment task showed that an ACTR-QN model produced both performance and mental workload data similar to the human results. In addition, different components of the multidimensional mental workload can be visualized with ACTR-QN. Mental workload modeling in ACTR-QN provides a new tool for human factors evaluation of mental workload.

Augmented-Reality-Based Indoor Navigation: A Comparative Analysis of Handheld Devices Versus Google Glass

Navigation systems have been widely used in outdoor environments, but indoor navigation systems are still in early development stages. In this paper, we introduced an augmented-reality-based indoor navigation application to assist people navigate in indoor environments. The application can be implemented on electronic devices such as a smartphone or a head-mounted device. In particular, we examined Google Glass as a wearable head-mounted device in comparison with handheld navigation aids including a smartphone and a paper map. We conducted both a technical assessment study and a human factors study. The technical assessment established the feasibility and reliability of the system. The human factors study evaluated human-machine system performance measures including perceived accuracy, navigation time, subjective comfort, subjective workload, and route memory retention. The results showed that the wearable device was perceived to be more accurate, but other performance and workload results indicated that the wearable device was not significantly different from the handheld smartphone. We also found that both digital navigation aids were better than the paper map in terms of shorter navigation time and lower workload, but digital navigation aids resulted in worse route retention. These results could provide empirical evidence supporting future designs of indoor navigation systems. Implications and future research were also discussed.

Augmented Reality-Based Indoor Navigation Using Google Glass as a Wearable Head-Mounted Display

This research comprehensively illustrates the design, implementation and evaluation of a novel marker less environment tracking technology for an augmented reality based indoor navigation application, adapted to efficiently operate on a proprietary head-mounted display. Although the display device used, Google Glass, had certain pitfalls such as short battery life, slow processing speed, and lower quality visual display but the tracking technology was able to complement these limitations by rendering a very efficient, precise, and intuitive navigation experience. The performance assessments, conducted on the basis of efficiency and accuracy, substantiated the utility of the device for everyday navigation scenarios, whereas a later conducted subjective evaluation of handheld and wearable devices also corroborated the wearable as the preferred device for indoor navigation.

Effect of driving experience on collision avoidance braking: an experimental investigation and computational modelling

Information technologies have been developed to facilitate driving performance and improve safety. However, there is a lack of computational methods that can take into account drivers’ adaptation to driving. That is, how behaviour changes with experience. Modelling the effect of driving experience on driver behaviour is important to the development of in-vehicle information technologies, because drivers at different skill levels may need different types or levels of assistance. Cognitive-architecture-based human performance modelling is a valuable method that can integrate different cognitive aspects underlying human behaviour such as skill levels and support quantitative simulation of behaviour. The study reported in this paper tested and examined computational models built in ACT-R (Adaptive Control of Thought-Rational) to account for the effect of driving experience on collision avoidance braking behaviour. The modelling results were compared with human data collected from a simulated driving experiment. The models produced braking behavioural results similar to the human results. Moreover, model predictions of three other emergent-braking scenarios were generally similar to and in the same order with the empirical results reported in previous studies. Future research can further integrate the method and results into intelligent driver assistance systems such as collision warning systems to better adjust the systems to the need of different drivers with different skill levels.

An Integrated Cognitive Architecture for Cognitive Engineering Applications

The increasing complexity of computational cognitive architectures may increase both their modeling capabilities and their difficulty to learn and use as cognitive engineering tools. This paper reports our work dedicated to enhance the usability and the cognitive engineering applicability of a complex computational cognitive architecture called QN-ACTR, which integrates two complementary architectures Queueing Network and Adaptive Control of Thought-Rational. The aim is to provide an easy-to-use interface and intuitive modeling that support both inexperienced and experienced users in using this complex and powerful architecture. The process of model development is greatly simplified with improved visualization and validation methods. The results were examined using heuristic evaluation. The benefits and practice implications are discussed.

Integrating Queueing Network and ACT-R Cognitive Architectures

Adaptive Control of Thought-Rational (ACT-R) and Queueing Network (QN) are two complementary but isolated cognitive architectures. The research reported in this paper aims to integrate the two architectures and benefit from their advantages so as to enhance cognitive modeling capabilities. The new combined architecture, named ACTR-QN, represents ACT-R as a QN whose servers are ACT-R modules and buffers and run the corresponding ACT-R functions. Task-specific knowledge and parameters are defined with ACT-R syntaxes. ACTR-QN provides real-time visualization of mental information processing in addition to ACT-R’s text output traces and is verified with 20 tasks that have been modeled by ACT-R. The steps and benefits of further integration are discussed.

Experimental Evaluation of Indoor Navigation Devices

Augmented reality (AR) interfaces for indoor navigation on handheld mobile devices seem to greatly enhance directional assistance and user engagement, but it is sometimes challenging for users to hold the device at specific position and orientation during navigation. Previous studies have not adequately explored wearable devices in this context. In the current study, we developed a prototype AR indoor navigation application in order to evaluate and compare handheld devices and wearable devices such as Google Glass, in terms of performance, workload, and perceived usability. The results showed that although the wearable device was perceived to have better accuracy, its overall navigation performance and workload were still similar to a handheld device. We also found that digital navigation aids were better than paper maps in terms of shorter task completion time and lower workload, but digital navigation aids also resulted in worse route/map retention.