Controls frameworks for human-autonomy integration (HAI) often treat human sources of information as highly reliable [1]. However, data from human sensing are quite variable, both because of human nature and limitations on sensor technologies. This paper focuses on estimating the degree of uncertainty in human sensed data, i.e., developing confidence metrics, and implementing those metrics in a HAI for target recognition. Here we demonstrate that applying such confidence estimates to sensed human data can mitigate effects of variability in human sensing and improve HAI performance.

As the proliferation of technology dramatically infiltrates all aspects of modern life, in many ways the world is becoming so dynamic and complex that technological capabilities are overwhelming human capabilities to optimally interact with and leverage those technologies. Fortunately, these technological advancements have also driven an explosion of neuroscience research over the past several decades, presenting engineers with a remarkable opportunity to design and develop flexible and adaptive brain-based neurotechnologies that integrate with and capitalize on human capabilities and limitations to improve human-system interactions. Major forerunners of this conception are brain-computer interfaces (BCIs), which to this point have been largely focused on improving the quality of life for particular clinical populations and include, for example, applications for advanced communications with paralyzed or “locked in” patients as well as the direct control of prostheses and wheelchairs. Near-term applications are envisioned that are primarily task oriented and are targeted to avoid the most difficult obstacles to development. In the farther term, a holistic approach to BCIs will enable a broad range of task-oriented and opportunistic applications by leveraging pervasive technologies and advanced analytical approaches to sense and merge critical brain, behavioral, task, and environmental information. Communications and other applications that are envisioned to be broadly impacted by BCIs are highlighted; however, these represent just a small sample of the potential of these technologies.

The study of brain-computer interfaces (BCIs) has undergone 30 years of intense development and has grown into a rich and diverse field. BCIs are technologies that enable direct communication between the human brain and external devices. Conventionally, wet electrodes have been employed to obtain unprecedented sensitivity to high-temporal-resolution brain activity; recently, the growing availability of various sensors that can be used to detect high-quality brain signals in a wide range of clinical and everyday environments is being exploited. This development of biosensing neurotechnologies and the desire to implement them in real-world applications have led to the opportunity to develop augmented BCIs (ABCIs) in the upcoming decades. An ABCI is similar to a BCI in that it relies on biosensors that record signals from the brain in everyday environments; the signals are then processed in real time to monitor the behavior of the human. To use an ABCI as a mobile brain imaging technique for everyday, real-life applications, the sensors and the corresponding device must be lightweight and the equipment response time must be short. This study presents an overview of the wide range of biosensor approaches currently being applied to ABCIs, from their use in the laboratory to their application in clinical and everyday use. The basic principles of each technique are described along with examples of current applications of cutting-edge neuroscience research. In summary, we show that ABCI techniques continue to grow and evolve, incorporating new technologies and advances to address ever more complex and important neuroscience issues, with advancements that are envisioned to lead to a wide range of real-life applications.

Fixed time lags within control systems are known to reduce the speed and accuracy of human-control actions. Further, the effects of variable time lags are not well-studied or understood but may exacerbate the negative effects associated with fixed time lags. Several studies have demonstrated mechanisms designed to combat the effects of time lag, which include adaptation, mathematical predictors and filters, and predictive displays. This experiment examined the effects of both fixed and variable time lag on a simulated-indirect-vision-driving task, as well as a possible mitigation (predictive display) for these effects. Results revealed that variable time lag significantly increased average lane offset more than fixed time lag, which indicates a decrease in driver accuracy. A predictive display significantly reduced lane offset and increased vehicle speed for both fixed and variable time lags. The predictive display also resulted in lower reports of operator workload. These results revealed the negative performance affects of variable time lag and demonstrated the utility of a predictive display to overcome the negative performance effects associated with fixed and variable time lags.

Autonomous mobility systems developed for unmanned ground vehicles may have additional benefits by enhancing system performance and reducing demands on operators for manned ground vehicles. This effort examines the potential impact of introducing autonomous mobility to control manned vehicles while operators performed secure mobility. Eleven Soldiers participated in an experimental task requiring concurrent control of a manned and an unmanned Stryker performing a road march, scanning of the local environment for targets, and planning of a reconnaissance route for a third simulated asset. The control of the manned vehicle was varied between autonomous and manual control and several speed and accuracy variables were examined for each task. Subjective measures of operator workload, stress, and motion sickness were also examined. The results support the potential benefits of incorporating autonomous mobility into manned platforms. In speed-matched conditions, autonomous mobility was associated with decreased manned vehicle mission time, faster operator reaction times to targets, greater instances of multitasking while under motion, and lower subjective operator workload measures than with manual driving. In conclusion, autonomous mobility technologies have the potential to free up resources from the vehicle operator and allow for better operator performance on tasks other than vehicle control.