Autonomous transport is on the way. Ships and cars are being fitted out to drive themselves. But is there a business in this, or will the hype fizzle out? Will people accept these machines?
Robots are always connected to people. Even a Mars rover’s tasks are planned each day by people. In this blog, I’m going to consider the relationship between people and autonomous vehicles on three interrelated levels: physical, commercial and social. Devices must work where intended, must be sellable, and must be acceptable to people.
When such devices are being used, the nature of the operating environment and the connection to people are as important as technical features. For example, robot vehicles already perform commercial tasks well, but within the enclosed environments of mining areas. People only venture into the vicinity of these giant robot trucks if they are sitting in a truck cabin themselves.
Money and safety at sea
Safety critical work is work in which human life would be endangered if something went wrong. Such work is generally governed by rules. Areas such as seafaring and motoring have their own sets of rules. Accidents are avoided if all parties comply with the rules and nothing surprising happens. Artificial intelligence complies with the rules set for it, but cannot adapt to unforeseen circumstances. In addition, a fault or accident may originate in AI itself. That is why control of autonomous transport devices operating among humans should not be left to AI alone. Human supervision is needed.
Labour is saved when one person can supervise several devices that are under the direct control of artificial intelligence. Employees no longer need to be at the mercy of field conditions, but work is done in the comfort and safety of a control centre. Such work does, however, involve new challenges. The scientific community has only recently begun to discuss the so-called transparency of artificial intelligence, i.e how easy it is for users to monitor the operations and functionality of AI. The supervision of self-learning AI – which can modify its own instructions – is particularly challenging. At the same time, there is a need to monitor and understand the operating environment of devices controlled by AI and the operation of sensor and communication technologies. Sensors of various kinds should be used to monitor activities in case some sensors do not work, or the signal is interrupted. For example, reliance on a GPS signal alone is unwise, since an external operator can disrupt positioning by generating a signal stronger than your own satellite signal.
The big challenge lies in the fact that operations must be economically viable in comparison to the traditional approach. For savings to be made, paying a control room team must be clearly cheaper than the wages of traditional field employees, since AI-controlled devices need new kinds of sensors and communication tools in order to function. More and more equipment is vulnerable to malfunctions and can no longer be serviced or repaired on-site, but a technical expert must be sent into the field. Personnel costs currently account for around six percent of a ship’s operating costs, but costs are also generated by the infrastructure required by people: an autonomous vessel does not require a toilet or kitchen.
Regardless of the challenges, both businesses and innovations will be created
There is still no certainty about which systems are most cost-effective when controlling ships, whether such systems are based in remote centres or on-deck. Seafaring is a conservative sector: attitudes to autonomous ships range from enthusiasm to scepticism. I believe that autonomous technologies will be useful. Even if commercially viable, unmanned ocean-going vessels are some way off, seafarers will soon benefit in various ways from sensor technologies and AI. Remote monitoring of ships is already happening.
For example, is it always necessary to maintain a 24-hour watch on the high seas? Fatigue, boredom and frustration all undermine safety. Perhaps it would be better if AI and the bridge kept watch at night, waking up the crew member on watch only if necessary. In addition, in challenging conditions new tools provide strong assistance in gaining situational awareness.
Change is slow to arrive. Good task planning, in which workers must be involved, is needed. They can provide information on challenges in the operating environment, which must be taken into account in the design of automated equipment.
Metro, automation and strong emotions: should we be afraid of fear itself?
My favourite transport system is the Metro in Paris. It connects people to every part of the metropolis within 45 minutes. Intuitive maps clearly show where you are going when walking in the platform area. When I lived in Paris, Line 14 was the only unmanned metro line. Somewhat unexpectedly, stepping into the carriage made me feel anxious. This feeling faded quickly, when the train lurched into motion. Among so many passengers, even subconsciously I understood that there was nothing to fear.
Later, I used the theoretical framework I had studied in Paris to study what local residents in Helsinki thought about a driverless metro. The theory states that people’s shared understanding of the world develops as they discuss new phenomena; the discussion in question is affected by existing structures of power and meanings in society. French social psychological theory builds a bridge between society and human understanding. I noted that people in Helsinki had negative attitudes towards the driverless metro, despite the media’s positive discussion of the issue. On the other hand, this negativism was reduced by facts about the automated metro. The idea of an automated metro was associated with experiences of unreliable computers, unemployment and dystopic images from science fiction.
My study of automatic metros provided the ideal basis or theoretical exploration, but was of little practical relevance. Helsinki never obtained a driverless metro and I now believe that preconceived ideas have limited influence on technology acceptance. People’s opinions are ultimately formed through direct use of the tool in question. This is demonstrated by my own experiences of line 14 of the Paris metro and the statistics: user experiences can be highly positive, even in the face of prejudices against robot technology.
So if the devices themselves are good, we shouldn’t worry too much about people’s preconceived ideas. However, fear should be dispelled through communications. If fear of the unknown is combined with problems or accidents, disproportionate damage may be done to the reputation of technology.
Automation and those being automated
I also think that technology firms do not need to be too worried about their workers, who, in principle, are the ones threatened by automation. The Finnish Seafarers’ Union is sceptical about autonomous ships in the same way as the metro drivers’ trade union was about the driverless metro. Despite this, the drivers were very open-minded about metro automation, at least when talking to an external researcher. There was no sign of a ‘rebellion’. On the other hand, the drivers were promised that they wouldn’t lose their jobs, but that their duties would change. In addition, perhaps the old drivers saw retirement approaching, while the younger ones were fascinated by involvement in a technological transition.
Senior Scientist, VTT
The writer studied the safety of autonomous ships as part of the AAWA project. A report of the safety analysis project completed alongside Aalto University is available here. The study on public opinion concerning Helsinki’s automated metro can be found here.
The first part of this three-part blog series discussed health care.
The last part, which will be published in February, will consider Human as a model for machines.