Is driving experience all that matters? Drivers' takeover performance in conditionally automated driving.

INTRODUCTION: In conditionally automated driving, drivers are allowed to engage in non-driving related tasks (NDRTs) and are occasionally requested to take over vehicle control in situations that the automation system cannot handle. Drivers may not be able to adequately perform such requests if they have limited driving experience. This study investigates the influence of driving experience on takeover performance in conditionally automated driving. METHOD: Nineteen subjects participated in this driving simulator study. The NDRTs consisted of three tasks: writing business emails (working condition), watching videos (entertaining condition), and taking a break with eyes closed (resting condition). These three NDRTs require drivers to invest high, moderate, and low levels of mental workload, respectively. The duration of engagement in each NDRT before a takeover request (TOR) was either 5 minutes (short interval) or 30 minutes (long interval). RESULTS: Drivers' driving experience and performance during the control period are highly correlated with their TOR performance. Furthermore, the type and duration of NDRT influence TOR performance, and inexperienced drivers exhibit poorer TOR performance than experienced drivers. CONCLUSIONS AND PRACTICAL APPLICATIONS: These findings have relevance for the types of NDRTs that ought to be permitted during automated driving, the design of automated driving systems, and the formulation of regulations regarding the responsible use of automated vehicles.

Estimation of the random incidence sound absorption coefficients of different size rectangular samples.

The sound absorption coefficient (SAC) of materials measured in a reverberation room is affected by both the intrinsic properties of the material and geometrical dimensions of the sample. A different size of the same material may produce a different SAC primarily due to the edge effect phenomenon. In this research, the experimental data from multiple laboratories was analyzed to evaluate the influence of the edge effect. An empirical function was established based on these measurement data and the linear relationship between the SAC and the relative edge length. Thomasson's method, the two geometric methods, and the analytical method were used to estimate the SAC of an absorber from measurements on a different size sample and compared with results obtained using the empirical function. The results show that the proposed empirical method is a reliable way to predict the SAC of a sample from measurements on a different size sample of the same material, which only requires the thickness, density, and size of the material.