Driver response to take-over requests in real traffic

Existing research on control-transitions from automateddriving (AD) to manual driving mainly stems from studiesin virtual settings. There is a need for studies conducted in realsettings to better understand the impacts of increasing vehicleautomation on traffic safety. This study aims specifically to understandhow drivers respond to take-over requests (TORs) in realtraffic by investigating the associations between 1) where driverslook when receiving the TOR, 2) repeated exposure to TORs, and3) the drivers’ response process. In total, thirty participants wereexposed to four TORs after about 5–6 min of driving with AD onpublic roads. While in AD, participants could choose to engage innon-driving-related tasks (NDRTs).When they received the TOR,for 38% of TORs, participants were already looking on path. Forthose TORs where drivers looked off path at the time of the TOR,the off-path glance was most commonly towards an NDRT item.Then, for 72% of TORs (independent on gaze direction), driversstarted their response process to the TOR by looking towardsthe instrument cluster before placing their hands on the steeringwheel and their foot on the accelerator pedal, and deactivatingautomation. Both timing and order of these actions varied amongparticipants, but all participants deactivated AD within 10 s fromthe TOR. The drivers’ gaze direction at the TOR had a strongerassociation with the response process than the repeated exposureto TORs did. Drivers can respond to TORs in real traffic. However,the response should be considered as a sequence of actions thatrequires a certain amount of time

Active sampling: A machine-learning-assisted framework for finite population inference with optimal subsamples

Data subsampling has become widely recognized as a tool to overcome computational and economic bottlenecks in analyzing massive datasets and measurement-constrained experiments. However, traditional subsampling methods often suffer from the lack of information available at the design stage. We propose an active sampling strategy that iterates between estimation and data collection with optimal subsamples, guided by machine learning predictions on yet unseen data. The method is illustrated on virtual simulation-based safety assessment of advanced driver assistance systems. Substantial performance improvements were observed compared to traditional sampling methods

Probing the tenets of attention theories via geometric visual illusions.

Although criticism of theoretical treatments of attention has accelerated recently, new formulations have not emerged. In this work, a list of attributes of attention that require theoretical treatment is presented. Attention theories and results in the empirical literature are categorized in terms of their contribution to the understanding of each of the attributes. In addition, the literature on visual illusions is discussed. Several theorists have proposed attentional explanations for visual illusions, including one account suggesting that attention increases the perceived size of objects. Four experiments tested this hypothesis. Three of the four experiments support the hypothesis that attention makes lines look longer, but do not support an attentional explanation for visual illusions. Experiment 3, in which a different attentional manipulation was used, produced no effects of attention on line-length judgments. The results of three of the four experiments demonstrate an unusual effect of attention on information processing. In these experiments, the information conveyed in both high- and low-attention conditions is the same, but the response in one attention condition is a recoding of the response in the other. A new stimulus-selection method for psychophysical experiments, the Smith-Cook Up-Down (SCUD) method, is developed. The SCUD method implements a formalization of the principle of placing trials to maximize the total information gathered about one or more parameters of the psychometric size-judgment function. Computer simulations show that the method is effective and relatively robust against poor prior assumptions about the parameters of the underlying psychometric function. It is also flexible in terms of the underlying distribution parameters about which it can maximize information. In the terms of psychophysical methodology, the research goal may emphasize the constant error (difference between the points of subjective and objective equality), the variable error (judgment consistency), or both. The new method and existing methods are discussed in terms of appropriateness for different experimental contexts. The SCUD method is successfully implemented in the fourth experiment in order to obtain better estimates of variability in line-length judgments while preserving the quality of estimates of the point of subjective equality (PSE).Ph.D.PsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/103581/1/9332059.pdfDescription of 9332059.pdf : Restricted to UM users only