192
Sensemaking in Safety Critical and Complex Situations
INTRODUCTION
There is an increase in the use of automation and autonomous solutions within transportation. According to The Oxford Dictionaries, autonomy is the right or condition of
self-government, and the freedom from external control or influence. Many researchers ( Relling et al., 2018) have discussed that the term is used differently in colloquial
language than in the technical definition and that it is interpreted in different ways
across industries. In this chapter, we emphasise that autonomy does not necessarily mean absence of human interaction. Often there is a strong need to design how
humans can make sense of automation failures and enact meaningful human control.
Automated systems operate by clear repeatable rules based on unambiguous
sensed data. An autonomous system can be a set of automated tasks, with interactions
with several sub-systems and/ or humans, with a specific degree/ level of autonomy.
Autonomous systems obtain data about the unstructured world around them, process
the data to generate information and generate alternatives and make decisions in
the face of uncertainty. Systems are not necessarily either fully automated or fully
autonomous but often fall somewhere in between ( Cummings, 2019). For example,
transportation can have different modes during a sea voyage. Outside the harbour,
in heavy traffic, it can be closely operated either by the remote control centre ( RCC)
or a captain/ driver, while in open waters with low traffic it can be controlled by the
computers or the autonomous system. Within the road traffic segment, the Society
of Automotive Engineers ( SAE) has defined a taxonomy on the levels of automation describing the expectations between automated systems and the human operator
( SAE, 2018). This is summarised in Table 12.1 below.
The levels apply to the driving automation feature( s) that are engaged in any given
instance of operation of an equipped vehicle. As such, a vehicle may be equipped
with a driving automation system that is capable of delivering multiple driving
automation features that perform at different levels. The level of driving automation exhibited in any given instance is determined by the feature( s) that are engaged
( SAE, 2018). Hence, autonomy is different across application areas; it varies over
time and is affected by the context.
To get a better overview and understanding, we start by looking at experiences
gained from ongoing research and/ or industry projects in the four transportation
Autonomy in Rail
198
..............................................................................................
Safety Challenges
199
.........................................................................................
Lesson Learned
199
.............................................................................................
Autonomy on Road
200
...........................................................................................
Safety Challenges
201
.........................................................................................
Lessons Learned
202
...........................................................................................
A Summary of MTO Safety Issues
203
........................................................................
Sensemaking to Support Meaningful Human Control
204
...........................................
Conclusion
205
.............................................................................................................
Acknowledgement
205
.................................................................................................
References
206
..............................................................................................................
Sensemaking in Safety Critical and Complex Situations
INTRODUCTION
There is an increase in the use of automation and autonomous solutions within transportation. According to The Oxford Dictionaries, autonomy is the right or condition of
self-government, and the freedom from external control or influence. Many researchers ( Relling et al., 2018) have discussed that the term is used differently in colloquial
language than in the technical definition and that it is interpreted in different ways
across industries. In this chapter, we emphasise that autonomy does not necessarily mean absence of human interaction. Often there is a strong need to design how
humans can make sense of automation failures and enact meaningful human control.
Automated systems operate by clear repeatable rules based on unambiguous
sensed data. An autonomous system can be a set of automated tasks, with interactions
with several sub-systems and/ or humans, with a specific degree/ level of autonomy.
Autonomous systems obtain data about the unstructured world around them, process
the data to generate information and generate alternatives and make decisions in
the face of uncertainty. Systems are not necessarily either fully automated or fully
autonomous but often fall somewhere in between ( Cummings, 2019). For example,
transportation can have different modes during a sea voyage. Outside the harbour,
in heavy traffic, it can be closely operated either by the remote control centre ( RCC)
or a captain/ driver, while in open waters with low traffic it can be controlled by the
computers or the autonomous system. Within the road traffic segment, the Society
of Automotive Engineers ( SAE) has defined a taxonomy on the levels of automation describing the expectations between automated systems and the human operator
( SAE, 2018). This is summarised in Table 12.1 below.
The levels apply to the driving automation feature( s) that are engaged in any given
instance of operation of an equipped vehicle. As such, a vehicle may be equipped
with a driving automation system that is capable of delivering multiple driving
automation features that perform at different levels. The level of driving automation exhibited in any given instance is determined by the feature( s) that are engaged
( SAE, 2018). Hence, autonomy is different across application areas; it varies over
time and is affected by the context.
To get a better overview and understanding, we start by looking at experiences
gained from ongoing research and/ or industry projects in the four transportation
Autonomy in Rail
198
..............................................................................................
Safety Challenges
199
.........................................................................................
Lesson Learned
199
.............................................................................................
Autonomy on Road
200
...........................................................................................
Safety Challenges
201
.........................................................................................
Lessons Learned
202
...........................................................................................
A Summary of MTO Safety Issues
203
........................................................................
Sensemaking to Support Meaningful Human Control
204
...........................................
Conclusion
205
.............................................................................................................
Acknowledgement
205
.................................................................................................
References
206
..............................................................................................................