Various automated systems require human supervision in complex environments which can be a monotonous task but still requiring a significant degree of attention. If those tasks are decisive to the process and work safety, then it is imperative that operators maintain adequate levels of alertness to execute necessary actions. Specially, the consequences of performance failure by operators in safety-critical task scenarios has increased concerns and drove important research since inattention or distraction could negatively affect the entire system including the integrity of the people on the system.

The AMT pool fund program has recently funded a tabletop analysis of different types of crash scenarios and the subsequent actions by different stakeholders. However, ATMA deployment risks are more than the ones during and after the crash. It is also critical to understand the potential major operational safety risks of ATMA deployment, before the crashes occur, and it is equally or even more important to identify countermeasures to prevent those crashes from happening. Identified and quantified risks and their impacts can further guide DOTs to prioritize these risks and work with DOT engineers to deploy corresponding countermeasures to ensure safety during ATMA deployment and generate additional product requirements.

The Concurrent Task Analysis (CoTA) builds upon Task Analysis (TA) theory and methods. TA was developed in the 1960s and had the initial focus of analyzing human performance. TA has since developed, influenced by the technical challenges in the Human-Computer Interaction (HCI). The CoTA follows a systems perspective rather than emphasizing human performance only: The flexibility of the plans of TA and its hierarchical structure allows modeling the expected behavior of a diversity of parts of the system.

Automated Driving Systems (ADS) offer the potential to reduce crash-related deaths and injuries, improve access to transportation, reduce traffic congestion and emissions, and improve productivity and quality of life for millions of people1. To realize these benefits, ADS vehicles utilize complex sensors, processing, algorithms, and controls to avoid many of the crash scenarios that occur today with human drivers and aspire not to introduce critical new crash scenarios. In addition to addressing safety in the design and development of ADS, methods for establishing and maintaining safe operations throughout deployment may also become an important part of the public’s acceptance for ADS-equipped vehicles.