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On the Road and Off the Map: Maps for Self-Driving Cars in an Over-Paved World

Even as autonomous cars provide a more radical change in patterns of mobility than any change in transportation, the amazing amounts of information that they synthesize suggest a way to process the rapid increase of roadways that have clogged much of the inhabited world.   Yet the new means that they bring to amassing of data to put places on the map comes at its cost.  Indeed, even the hopes to provide a high-density record to be able to navigate roadspace leaves an eery imprint for what it leaves out, and the ghostly skeletal system of roadways that they try to trace, which raise questions about the sort of space that maps serve to embody.  For rather than trace the deserted roads of an imagined landscape ready to explore, the streets blanched of a world where discoveries are made suggest a tracery of recorded tracks removed from local testimony or a concept of place.

The first promise that paved interstates would bring good roads everywhere promised an opening up of national spaces and the economy, just over a century ago–when roads were not uniformly paved at the same level, a situation that the Good Roads movement sought to remedy by calling attention to the poorly paved nature of the nation before World War One, and the lesser wealth associate with unimproved roads.


National Highways to Bring Good Roads Ass'n.png

National Highways to bring about Good Roads Everywhere (1895/1913)

The promise of self-driving cars to internalize an image of the roadways provides a sense of driving experience presupposing pretty perfect road conditions, but promises to provide a smoother sense of driving, removed from accident.  For the patterns of the maps for self-driving cars, rather than fit into a record of inhabited space, or of the natural world, seem to pose propositions of the existence of a purely driven space, occupied less by cars or at least not by passengers but by a visualization of road conditions, in ways that eerily suggest less of a world that can be filled in as a broader canvas of living or nature, but a purely man-made world.   Despite the considerable appeal of a crash-free world of the automated vehicle that the huge demand for self-driving cars promises, the high data density maps being developed to place space on a map presents a terrifyingly circumscribed landscape of roadways that demand attention as a way of looking at the world–and symbolizing space.  Perhaps this is largely due to their machine-readable nature.  But it also seems terrifying insofar as one rarely appreciates the costs for what is left off the map, and the removal of the map from the roadways–and the alienated image of the roadways that they seem to present.

For like the paths of pilgrimage of medieval times, which viewed isolated itineraries with no reference to geographic space, or the disembodied paths of nautical charts whose rhumb lines, drawn over the world, suggest navigational itineraries drawn across the Mediterranean, the skeletal tracery of the roadscape suggests a sense of routes removed from testimony and disembodied, distilled to the information of the roadways and a purely anthropogenic world and removed from its context, as if roads remain oddly stripped of their local references.  If places are where we inhabit, the disembodied nature of the datasets of the maps for self-driving cars are removed from them, and suggest links around them that lack any actual testimony.


ITaly as Nautical Compilation


And despite the possible benefits for autonomous cars, maps made for ensuring safe driverless driving test the not only the huge amounts of data that enter in maps, as well as the problems of prioritizing selective data, that raise questions not only about the richness of these high density maps, but the sorts of world that the arrival of autonomous cars register.


1. The eerily ghostly roadways of the maps made for self-driving cars seem quite proper:  for they track the road as inhabited by the car, and not by the spaces around them.  If the intellectual property of tools for processing and formulating driverless maps stands at the cutting edge of recent lawsuits, is the increasingly ghostly character of maps made for driverless cars not also a serious cost?  The fragmentary picture of strings of man-made space erase the notion of a pilgrimage to a detention, providing a real-time record of roads’ obstacles, speed rates and traffic density, offering clues for how the car can move across and over space, but does so in the context of distilling the roadways to the basic criteria that cars will most especially need to know, and far less about the spaces that we might visit.  While made for autonomous driving vehicles, the absence of testimony and the lack of differentiation among places seems poignantly and particularly wanting.

The roadways that entangle much of the inhabited earth with transit corridors demand a complicated set of tools for their mapping, but does the erasure of the experience of driving, converted into a matrix of data, also register a deep danger in how we have come to inhabit space?  For if the proliferation of interchanges show the growth of roadways and arteries of automotive transport, some including up to fifteen lanes, branching out into eight directions, inspiring one netizen to ventriloquize, “Car GPS: ‘I can only take you this far, the rest is up to you,‘” imagining frustrated befuddlement at this Chongqing interchange, whose curving on-ramps and shifting elevations can hardly  be untangled by data from motion sensors or GPS.   The radically curtailed influence that the map offers readers stands in uneasy juxtaposition with the fears mazes of manmade roadways may even outstrip navigational capabilities.  One imagines not only the sort of dialogue that might occur with automated navigational services as Siri, their GPS coordinates overloaded by the multi-directional arrival of cars on different lanes in the freeway exchange, but the difficulty–and the need–for the data density of a map for automated cars that would process the possible courses of lane changes and arcs of on-ramps in ways that the driverless car would be able to navigate.




2. All maps are made to meet demands, and the expanding market for maps for self-driving cars is no exception.  But if we have become able to map traffic and routes for some time, the ghostly sense of inhabitation in maps for self-driving cars seem worth reflection–for the image of the world they create; the ethics of mapping the road conditions, and how theses maps orient us to the world. Fort he intelligence of such maps, made to be machine-read rather than read by humans,  propose a different notion of the “inhabited world” that is in truth increasingly closer to the road-covered world that we increasingly inhabit.  While the safety of such maps effectively allow us to be passengers in such self-driving cars, they also render a new sense of the worlds in which we are inhabitants.  For the haunting ghostly worlds that maps for self-driving reproduce and create provide an odd record of our increasingly paved-over world, where roads-free landscape is ever shrinking.  Are the maps for self-driving cars a premonition of a paved over future?

Driving is among the most familiar extension of an embodied experience, and the most familiar experience of navigation and way-finding that we have today.  But as maps are increasingly present behind the wheel, as it were, and built into many cars, today, both in the form of dashboard monitors, handheld devices, and disembodied voices, the relation of the map to the experience of driving has changed.  As maps have become data and datasets, we have no only constructed far more visually elegant renderings of roads and driving conditions.  As the maps for driving have departed from the over-folded pieces of paper, often ripped or worn at the crease, that used to be stuffed into the romantically named “glove compartment” and migrate underneath sun visors or into  the side-compartments on front doors, into interactive experiences that we read, they have in many ways transcended our abilities for attention.  And the increased demands for attention in our society and even for our drivers has led to a new market not only for for data rich maps, but for the maps that would help guarantee the safety of self-driving cars.

In an age where Google dominates mapping, creating the tools to develop maps for autonomous vehicles–“self-driving” cars that navigate by LiDAR software, real-time radar and laser sensors, streaming data libraries and programs–


perched processing directions for URBER.png


–which stands to prove the most important mapping innovation since the satellite, and perhaps the most valuable ever, as over thirty companies are applying to test-run their own self-driving cars in California, seat of the future, and the winner seems destined to be the one with the most complete and sophisticated mapping tools.  The tools planned to allow the cars to navigate real space don’t provide anything similar to a recognizable landscape, but Google’s driverless car division–Waymo–used the code-name ‘Chauffeur’ to refer to the armory of LiDAR tools as if to humanize the tools by which autonomous cars will be instilled with the ability to develop an effective cognitive relation to space.   Although autonomous cars may threaten to overturn the hegemony of Google has retained as a mapping engine,  the new remapping of the freeways also threatens a changed relation to most all extra-urban off-road space.   Is the growth of the market for self-driving cars not in itself emblematic of a new relation to space, where the car is less the instrument of exploration or navigation–the Keruoac’s image of being “on the road”–but a now bulky mode of transit and commuting, whose increasingly mechanical modalities of operation seem to be best performed by an artificial driver, built-in to the car.  Even as it is foretold AI is destined to replace increasing numbers of workers with world-changing effects that are only “50 or 100 years away,” we have kept fears of economic shocks and needs for massive retraining at bay, but face a profound fear of decreased human agency.

The diminished agency of the human is perhaps no more apparent than in the rapid race to design maps for self-driving cars–maps read by cars to familiarize themselves with traffic conditions and their routes, in ways that dispense with human judgment behind the wheel–one of the most privileged sorts of agency in existence–even if the maps for self-driving cars are now limited to the most mechanical forms of transportation on “smart highways” and shipping routes.

What sort of intelligence is lost, one might well ask, and what gained?

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Filed under 3-D maps, autonomous cars, HD Maps, machine-readable maps, self-driving cars