These data support the therapeutic role of radioembolization for the treatment of unresectable ICC with good efficacy and an acceptable safety profile.
A PC-based training program (Road Awareness and Perception Training or RAPT; Pradhan et al., 2009), proven effective for improving young novice drivers' hazard anticipation skills, did not fully maximize the hazard anticipation performance of young drivers despite the use of similar anticipation scenarios in both, the training and the evaluation drives. The current driving simulator experiment examined the additive effects of expert eye movement videos following RAPT training on young drivers' hazard anticipation performance compared to video-only and RAPT-only conditions. The study employed a between-subject design in which 36 young participants (aged 18-21) were equally and randomly assigned to one of three experimental conditions, were outfitted with an eye tracker and drove four unique scenarios on a driving simulator to evaluate the effect of treatment on their anticipation skills. The results indicate that the young participants that viewed the videos of expert eye movements following the completion of RAPT showed significant improvements in their hazard anticipation ability (85%) on the subsequent experimental evaluation drives compared to those young drivers who were only exposed to either the RAPT training (61%) or the Video (43%). The results further imply that videos of expert eye movements shown immediately after RAPT training may improve the drivers' anticipation skills by helping them map and integrate the spatial and tactical knowledge gained in a training program within dynamic driving environments involving latent hazards.
Automated driving systems (ADS) partially or fully perform driving functions. Yet, the effects of ADS on drivers’ visual sampling patterns to the forward roadway remain underexplored. This study examined the eye movements of 24 young drivers during either manual (L0) or partially automated driving (L2) in a driving simulator. After completing a hazard anticipation training program, Road Awareness and Perception Training, drivers in both groups navigated a single simulated drive consisting of four environment types: highway, town, rural, and residential. Drivers of the simulated L2 system were instructed to keep their hands on the steering wheel and told that the system controls the speed and lateral positioning of the vehicle while avoiding potential threats on the forward roadway. The data indicate that the drivers produced fewer fixations during automated driving compared with manual driving. However, the breadth of horizontal and vertical eye movements and the mean fixation durations did not strongly support the null results between the two conditions. Existing hazard anticipation training programs may effectively protect drivers of partially automated systems from inattention to the forward roadway.
A previous study demonstrated that the administration of expert eye movement videos following hazard anticipation training can improve the proportion of latent hazards anticipated by young drivers compared to control conditions. The current driving simulator study sought to examine whether the improvements observed in the previous study were merely due to drivers' exposure to videos of the simulated driving scenarios with expert eye movement overlays immediately prior to evaluation, or whether modeling the accuracy of eye movement behavior can lead participants to internalize hazard anticipation skills more effectively. In a between-subject design, 36 drivers (18-21 years) were assigned to one of three experimental conditions -training only, training plus expert eye movements or training plus novice eye movements. All participants navigated four unique driving scenarios, each with their eye movements tracked and recorded. Analyses of the eye movement data showed that young drivers who saw the expert eye movement (accurate) videos immediately following training anticipated a substantially greater proportion of latent hazards compared to the young drivers that saw novice eye movement (inaccurate) videos following training. The data provide some evidence that drivers were able to successfully map and incorporate correct hazard anticipation glance behavior into their mental models. The findings present some implications for the design and evaluation of eye movement-based training interventions.
Automated driving systems (ADS) partially or fully perform or assist with primary driving functions. According to SAE J3016 (SAE, 2016), ADS can subsume driving tasks traditionally reserved for humans, ranging from L0 (no automation) to L5 (full automation), creating varying degrees of driver interaction and responsibility. However, the literature on human-automation interaction indicates that human operators may perform at a suboptimal level when interacting with automated support systems (Parasuraman & Riley, 1997), reducing the net benefit that automation can bring while also simultaneously increasing the potential for unforeseen human errors. Yamani and Horrey (in press) proposed a theoretical framework of human-automation interaction building upon a human information-processing model (Wickens, Hollands, Banbury, & Parasuraman, 2013) that accounts for human performance when interacting with varying types and levels of automation (Parasuraman, Sheridan, & Wickens, 2000). Following the model by Yamani and Horrey (in press), we hypothesized that when the ADS is perceived to be reliable, drivers engaging with such systems (e.g. L2) would exhibit eye movements no better or worse than the drivers engaged with manual or L0 driving since the drivers allocate their reserved or spare resources to other driving-irrelevant activities such as mind wandering or task irrelevant thoughts (Yanko & Spalek, 2014). The current driving simulator study compared young drivers’ eye movements across four unique scenarios in either L0 or L2 driving systems. We asked participants to complete a three-phased skill-based training program (RAPT-3; see Unverricht, Samuel, & Yamani for review) proven effective to improve young drivers’ ability to anticipate latent hazards, immediately followed by the evaluation of their eye movements in either L0 or L2 systems using a head-mounted eye tracker and a driving simulator. Participants in the L2 condition were instructed that the system detects and mitigates existing and latent threats on the forward roadway while maintaining appropriate speed and lateral positioning for the duration of the drive. To ensure similarity between both systems, L2 participants were required to position their hands on the steering wheel and feet above the pedal. No hazards materialized in any of the four driving scenarios. Data showed similar breadths of eye movements for the drivers of the L2 and L0 systems both horizontally [M = 36.5 vs. 36.3 pixels; L2 and L0, respectively] and vertically [M = 26.9 vs. 34.5 pixels] and no difference in mean fixation durations [M = 367 vs. 333 ms for L2 and L0 conditions]. However, data indicated substantial differences between L0 and L2 conditions for number of fixations, with L2 drivers fixating less frequently than L0 drivers, [M = 687 vs. 796 fixations, t (22) = 2.53, B10 = 3.23]. The results imply that L2 drivers may sample information from the forward roadway less often than L0 drivers, suggesting the mobilization of spare resources for non-driving related tasks. Future research should examine the relationship between conveyed system reliability and attention allocation for drivers of ADS with different automation levels. In summary, the current results support Yamani and Horrey’s model and offer potential implications for the design of autonomous systems and the NHTSA automation guidelines to consider the perceived reliability of lower level ADS towards ascribing the role of the driver when the driving task is either partially or fully automated.
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