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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.

Objective This paper investigates driver engagement with vehicle automation and the transition to manual control in the context of a phenomenon that we have termed vicarious steering—drivers steering when the vehicle is under automated control. Background Automated vehicles introduce many challenges, including disengagement from the driving task and out-of-the-loop performance decrement. We examine drivers’ steering behavior when the automation is engaged, and steering input has no effect on the vehicle state. Such vicarious steering is a potential indicator of engagement for evaluating automated vehicles. Method A total of 32 female and 32 male drivers between 25 and 55 years of age participated in this experiment. A 2 × 2 between-subject design combined control algorithms and instructed responsibility. The control algorithms (lane centering and adaptive) were intended to convey the capability of the automation. The adaptive algorithm drifted across the lane center when latent hazards were present. The instructed levels of responsibility (driver primarily responsible and automation primarily responsible) were intended to replicate the admonitions of owners’ manuals. Results The adaptive algorithm increased vicarious steering ( p < .001), but instructed responsibility did not ( p = .67), and there was no interaction between the algorithm and the responsibility ( p = .75). Vicarious steering was associated with an increase in transitions to manual control and glances to the road but was negatively associated with driving performance immediately after the transition to manual control. Conclusion Vicarious steering is a promising indicator of driver engagement when the vehicle is under automated control and automation algorithms can promote engagement.

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Mind wandering is a poorly understood phenomenon that can undermine driving safety. Driving performance measures have been found to be associated with mind wandering (e.g., steering wheel movements, standard deviation of lateral position, and speed variation). However, no one measure can fully describe the driver behavior associated with mind wandering. Therefore, in this paper we explore the effect of mind wandering on nine steering measures with data collected from a study that included nine drivers over two sessions of driving over five days. Participants were periodically probed to report their attentional state–whether they were mind wandering or focusing on the task. We used two dimensionality-reduction techniques—Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE)—to visualize the dimensions underlying the nine measures. Comparing PCA to t-SNE highlights the benefits of t-SNE in revealing the fine structure that differentiates driving behavior. These visualizations show that a) driver engagement increased during roadway curve segments, and b) mind wandering manifests itself through several types of steering behavior.