No abstract

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 study examines how driving styles of fully automated vehicles affect drivers’ trust using a statistical technique—the two-part mixed model—that considers the frequency and magnitude of drivers’ interventions. Background: Adoption of fully automated vehicles depends on how people accept and trust them, and the vehicle’s driving style might have an important influence. Method: A driving simulator experiment exposed participants to a fully automated vehicle with three driving styles (aggressive, moderate, and conservative) across four intersection types (with and without a stop sign and with and without crossing path traffic). Drivers indicated their dissatisfaction with the automation by depressing the brake or accelerator pedals. A two-part mixed model examined how automation style, intersection type, and the distance between the automation’s driving style and the person’s driving style affected the frequency and magnitude of their pedal depression. Results: The conservative automated driving style increased the frequency and magnitude of accelerator pedal inputs; conversely, the aggressive style increased the frequency and magnitude of brake pedal inputs. The two-part mixed model showed a similar pattern for the factors influencing driver response, but the distance between driving styles affected how often the brake pedal was pressed, but it had little effect on how much it was pressed. Conclusion: Eliciting brake and accelerator pedal responses provides a temporally precise indicator of drivers’ trust of automated driving styles, and the two-part model considers both the discrete and continuous characteristics of this indicator. Application: We offer a measure and method for assessing driving styles.

When automobiles were first introduced in the early 1900s, poor communication and unsafe interactions between drivers and other road users generated resistance. This created a need for new infrastructure, vehicle design, and social norms to mitigate their negative effects on society. Vehicle automation may lead to similar challenges as drivers are supplanted by machines, potentially eliminating social behaviors that serve to smooth on-road communication and coordination. Through a review of communication, robotics, and traffic engineering literature, we explore the mechanisms that allow people to communicate on the road. We show the sensitivity of road users to signals that are sent through vehicle motion, suggesting a need to design vehicle automation kinematics for communication and not just external lighting signals. The framework further points to interdependence in communication where road users modulate their behaviors concurrently to exchange information and develop common ground. Designing automation to support common ground may smooth negotiations by generating interpretable signals in ambiguous situations. We propose a process to make automation observable and directable for other road users by considering vehicle motion during development of algorithms, interfaces, and interactions. Road users will be incidental users of vehicle automation-users whose goals are not directly supported by the technology-and poor communication with them may undermine the safety and acceptance of vehicle automation. As the reach of automation grows, communication among humans and machines may fundamentally change social interactions, requiring a framework to guide the process of making automation interactions smooth and natural. INDEX TERMS Human factors, automation, autonomous vehicles, human-robot interaction, pedestrian.