Autonomous Last Mile

Autonomous vehicles are thought by many to be the future of mobility. Expected to be safer than human drivers, they are envisaged to be able to navigate the world without distraction or fatigue. 

However, the current truth of the deployments of autonomous systems we see in the world now is that they are not truly taking over human labour. These systems, often expected to take the ‘Dull, Dirty and Dangerous’ tasks from humans and leaving them to do creative and organisational tasks have been shown to actually increase human injuries in some instances. This is because, in general, these systems start by taking the easiest tasks and leave only the complex, stressful, and time-sensitive work for the humans. In another strand of development, proponents of 5G and beyond have touted remote teleoperation as a solution to situations that cannot be automated. Socially, there is an overall rise in the Gig Economy, where precarious workers (often migrant labour) are renumerated for each individual piece of productivity rather than being offered a stable salary. 

This project gives an interactive vision of a future where these current trends come together to create an unequal, unsafe, and unwanted vision of our future society. By exposing people today to possible future situation where they must quickly perform only the most stressful and complex of mobility tasks, in quick succession, under economic and social constraints that give them no other option we hope to encourage designers, developers and end users of these technologies to ensure that it does not come to pass.

In this speculative and fictional scenario a new operator is being onboarded to perform such piecemeal labor of teleoperating autonoms vehicles when the situation is outside of their abilites or the legal constraints of their operation. Training is being conducted using the same high-level XR headset they will use during live operations. Indeed, these training events may or may not be live operations used as training and evaluation scenarios for human-autonomy teaming.