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.
We consider a large dataset of real-world, on-road driving from a 100-car naturalistic study to explore the predictive power of driver glances and, specifically, to answer the following question: what can be predicted about the state of the driver and the state of the driving environment from a 6second sequence of macro-glances? The context-based nature of such glances allows for application of supervised learning to the problem of vision-based gaze estimation, making it robust, accurate, and reliable in messy, real-world conditions. So, it's valuable to ask whether such macro-glances can be used to infer behavioral, environmental, and demographic variables? We analyze 27 binary classification problems based on these variables. The takeaway is that glance can be used as part of a multi-sensor real-time system to predict radio-tuning, fatigue state, failure to signal, talking, and several environment variables.
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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