Since the introduction of automobiles in the early 1900s, communication among elements of the transportation system has been critical for efficiency, safety, and fairness. Communication mechanisms such as signs, lights, and roadway markings were developed to send signals about affordances (i.e., where and when can I go?) and constraints (i.e., where and when can I not go?). In addition, signals among road users such as the hand wave have emerged to communicate similar information. With the introduction of highly automated vehicles, it may be necessary to understand communication signals and apply them to vehicle automation design. However, the question remains: how do we identify the most important interactions that need to be considered for vehicle automation? We propose a method by which we examine the timing of existing vehicle–pedestrian interactions to make conclusions about how the use of time and space can be used as a communication tool. Videos were recorded at representative intersections and crossings in a mid-sized, Midwestern U.S. town. The intersections were chosen based on their potential to elicit interactions with pedestrians and their ubiquity (e.g., four-way stop). Videos were then coded to describe the interactions between vehicles and pedestrians. A focus of this coding was the short stop—stopping before a crosswalk to communicate yielding intent to a pedestrian—which was defined as the time from when the vehicle began to accelerate, after slowing down, to when it reached the crosswalk. Results revealed evidence that vehicle kinematic and spatial cues signal the driver’s intent to other road users.
This study examined how short sleep impacts dietary consumption in adolescents by testing whether experimentally shortening sleep influences the amount, macronutrient content, food types, and timing of food consumed. Ninety-three adolescents completed a within-subjects crossover paradigm comparing five nights of short sleep (6.5-hour sleep opportunity) to five nights of Healthy Sleep (9.5-hour sleep opportunity). Within each condition, adolescents completed three multiple-pass dietary recalls that recorded the types, amount, and timing of food intake. The following outcomes were averaged across days of dietary recall within condition: kilocalories, grams of carbohydrates, fat, protein, and added sugars, glycemic load of foods, and servings of specific types of foods (low-calorie drinks, sweetened drinks, fruits/vegetables, meats/proteins, processed snacks, “fast food” entrees, grains, and sweets/desserts). Timing of consumption of kilocalorie and macronutrient outcomes were also examined across four noncumulative time bins: 06:00–10:59, 11:00–15:59, 16:00–20:59, and 21:00–01:00. Adolescents slept 2 h and 20 min longer in Healthy Sleep than in Short Sleep (p < .0001). While in Short Sleep, adolescents ate more grams of carbohydrates (p = .031) and added sugars (p = .047), foods higher in glycemic load (p = .013), and servings of sweet drinks (p = .023) and ate fewer servings of fruits/vegetables (p = .006) compared to Healthy Sleep. Differences in consumption of kilocalories, fat, and carbohydrates emerged after 9:00 pm (ps = .012, .043, .006, respectively). These experimental findings suggest that adolescents who have insufficient sleep exhibit dietary patterns that may increase the risk for negative weight and cardiometabolic outcomes. Future health promotion efforts should include promoting optimal sleep to increase healthy dietary habits.
Increasingly vehicle automation may convey greater capability than it actually possesses. The emergence of highly capable vehicle automation (e.g., SAE Level 4) and the promise of driverless vehicles in the near future can lead drivers to inappropriately cede responsibility for driving to the vehicle with less capable automation (e.g., SAE Level 2). This inappropriate reliance on automation can compromise safety, and so we investigated how algorithms and instructions might mitigate overreliance. Seventy-two drivers, balanced by gender, between the ages of 25 and 55, participated in this study using a fixed-base driving simulator. Drivers were exposed to one of three vehicle steering algorithms: lane centering, lane keeping, or an adaptive combination. A gaze tracker was used to track eye glance behavior. While automation was engaged, participants were told they could interact with an email sorting task on a tablet positioned near the center stack. Changes in roadway demand-traffic approaching in the adjacent lane-varied across the drive. Instructions indicating the driver was responsible, in combination with the adaptive algorithm, led drivers to be particularly attentive to the road as the traffic approached them. These results also have implications for evaluating more capable automation (SAE Levels 4 and 5), where drivers need not attend to the road: unnecessary attention to roadway demands might indicate lack of trust and acceptance of control algorithms that guide driverless vehicles.
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