Tag Archives: 3-D maps

On the Road: Maps for Self-Driving Cars?

The notion that AI is destined to relace increasing numbers of workers in unknown ways just “50 or 100 years away” suggests not only fears of economic shocks and needs for massive retraining, but a profound fear of decreased human agency.  This is perhaps no more evident than in maps for self-driving cars.  If maps remain among the most human of creations to orient viewers to place and space, creating a sense of spatial continuity we can’t otherwise readily perceive, the interest in making maps singly for self-driving cars is so removed from human skills of map-reading to make them profound alienating landscapes.  These are not landscapes occupied by humans, after all, but may suggest an increasingly common aspect of the landscapes which we are creating for cars.

We’ve long expected maps orient human readers to the continuity of inhabited space that affirm regional integrity and offer needed cognitive way-finding tools.  The continuity maps create makes them among the most humanistic of documents, extending cognitive skills and establishing needed frameworks for understanding space and place as less than abstractions but in concrete terms.  The cognitive magic of embodying spatial relationships to be grasped at a glance functions in large parts by the success of their selective conventions as tools to place ourselves in a form of vision accessible to the human eye, condensing markers that often lie in the land to a form we can readily engage.  The new maps that promise to orient self-driving cars to the roadways rather focus on charting data on flow, and the constraints of space, rathe than they organize space as a recognizably continuous record of the roadways.  They offer what one might call the “First World” perspective on our changing inhabited space.

And so the dramatically curtailed nature of the mis en scène of the maps for made for self-driving cars offer seems of interest:  to be sure, such maps make less appeal to human cognition, offering a purely machine-readable sense of place and of space oddly removed from human habitation.  But they condense and transpose a human relation to the landscape into readable form that, in the end, serve to orient us to how we navigate and voyage through space.   Is this mapping from a machine point of view, or is evidence of the new dominance of road space and roadways in an increasingly over- inhabited world, where place is eroded within a continuum of constant transit, far more sensitive to flow than place or space?

A rendition of the spatio-temporal database. Source: Zenrin Co.

A rendition of the spatio-temporal database. Source: Zenrin Co.

The uncanny absence of engagement with the natural–and the transformation of the map, as it were, to a network of man-made roads that is removed from a human landscape.  Once stripped of the real mosaic of place and removing human settlement from a web of forests, landscapes, or wilderness–and indeed entirely from the natural and non-man-made world:–the concentration on roads filter out any sense of motion through space from any experience outside a car.  In an overly detailed armature of isolated itineraries, organized from discrete data points, the maps designed for autonomous cars present a denuded landscape, stripped of context that is not immediate, and without any accreted human knowledge preserved in place-names.  If the map is often described as a text, Hi-Def maps replace text with rich data points and a feeds of real-time information from sensors.  Rather than locate oneself to space by successive markers in a landscape along a road or pathway–



D.T. Valentine and George Hayward, Common Lands between three- and six-mile stones (1799) (Museum of the City of New York)

–the condensation of road signs and driving conventions seek to allow a car to orient itself to space in ways that strip space of its tactile surroundings, doing the work in many ways for orienting oneself to signs located along the roads.  Rather than chart location for a viewer, HD maps for self-driving cars are sensitive to capturing the constraints and flows by which a car can orient itself to the shifting traffic and crowded lanes that organize the changing terrain of highway space, an area to which so much increasing land cover has been dedicated across the inhabited world:  the HD maps take the space of the highways something like as a proxy for the inhabited world.  As records of the shifting space of the speedways, these maps promise to register the complex calculus of highways to promise a safe trip for future passengers.  If J. B. Jackson famously suggested the rise of “auto-vernacular” landscapes in the United States whose deceptive sense of ‘placelessness’ reflected the changing middle class lifestyles of suburban tract homes and suburban subdivisions and strip housing located along highways, the maps for self-driving cars are cultural landscapes stripped of signs of habitation save the roadways.

The provision of these maps by sensors register real-time accounts of spatial relationships that suggest the range of tools by which we have come to orient ourselves to the world’s paved highways:  yet at a time when Google Maps register space universally, from a synthesis of local surveys and satellite imagery, the maps for self-driving cars provide a downloadable cognitive framework, preserved on the cloud, for how cars can orient themselves to space by surveying their real positions on roadways, and depend less on human drivers in an age when, the distracted driving is so widespread, according to analytics company Zendrive, that American drivers use phones on 88% of the journeys they make on the road–and in spend 3.5 minutes per hour looking at their phone, and not only for directions.  In an age of mass-distraction where drivers can’t be as trusted to watch the road, maps for self-driving cars are perhaps needed to reduce risk–even two-seconds of distraction on your phone increases individual drivers’ risk by 20 percent; the National Highway Traffic Safety Administration recently counted almost 3,500 people deaths in distraction-related car crashes in the United States in 2015, and almost 400,000 injured, and the problem stands to worsen as after a recent trend toward declining numbers of driving fatalities, fatalities due to distracted driving are substantially rising, and especially among teens.  The statistics are unclear, as accidents involving cell phones are under-reported by authorities.  At a time when paper highway maps are no longer consulted, and in fact rarely sufficient–and navigation has migrated onto the phone, if not Siri, it’s no surprise the need and market for maps for self-driving cars has so markedly grown.

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The response to such distraction has been to provide a newly comprehensive map able to interface with self-driving cars, in ways that might allow our existing habits of distraction to grow.  In a sense, this is a local–as much as a universal–recuperation of the perspective of the single surveyor, providing a local automobilistic model of mapping with advantages one can’t gain from satellite/LiDAR alone, or a filling in of the gaps.   But the maps for self-driving cars are not made for human audiences, and as much as orienting individuals, offers perspectives of an inhabited world that is defined only by the extent of traffic on roadways–and which consumes increased cognitive attention of commuters–but which offers an extremely alienated perspective on the world.

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