Tag Archives: Highway Maps

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

Self-driving or autonomous cars promise a change in patterns of mobility more radical than any change in transportation.  While depending on maps, the maps made for self-driving cars are perhaps unlike any other:  not made or designed for human eyes.

They suggest a deep change in mapping, assembling a machine-readable record of each edge of the road.  At the same time as roads are scanned, integrated with LIDAR imagery of the environment, and augmented with real-time feedback loops in ways that seem to provide a virtual 1:1 map of automative environments in which cars can navigate.  Yet how intelligent are these “intelligent maps” by which self-driving maps integrate and position themselves in the space networks of roads?  As Shannon Mattern has argued, “With the stakes so high, we need to keep asking critical questions about how machines [are able] to conceptualize and operationalize space” that can recognize the human actors in space, and how the increased role of these networks of mapping serve as actants–shifting the networks of on-road behavior, or how, as Mattern puts it, “artificial intelligences, with their digital sensors and deep learning models” that perpetuate one image of space will “intersect with cartographic intelligences and subjectivities beyond the computational ‘Other'”  How will such maps, put differently, register people who also occupy the sidewalks, and the other cars on the highway (either as drivers or passengers), and how will they be effected by them?

The lack of a clearly sensory cartography of place is not only inherent in the ghostly nature of Lidar views of streets, and street-settings, but they are rooted in the trust we are increasingly inclined to assign machines for reading space in an accurate and comprehensive way, and indeed a manner with greater continuity and precision than many maps can hope to contain.  The amazement at the possibilities posed by mechanical sensing is, in a sense, pushed to new limits in the promises of self-driving cars, who have quickly gained multiple evangelists.  We already have cars able to signal their approach of the edges of traffic lanes, altering their human drivers of impending danger.  The promises of self-driving cars have generated increasing optimism in the United States and Japan, as the next generation of driving vehicles in a culture ready to embrace the new, perhaps because they promise the very possibility of constant motion in a country of speed.  But by removing routes of human motion and how humans move through road systems from direct intelligence, the maps that are being designed for autonomous vehicles to navigate the roadways of America and beyond suggest a new nature of space, as much as of transportation or transit:  and the maps for self-driving cars, while not designed for human readers, suggest a scary landscape rarely open to surprises and eerily empty of any sign of human habitation.

The maps for autonomous vehicles are, commonsensically, absent of human presence in the automative landscape they reveal–and that has grown up around them.  They are the creation of an over-paved world, and also on the readiness to accept the growth of this over-paved world:  while based on LIDAR sensing, much of the sensing that goes into their construction or appears in rough cuts will end up on the cutting-rom floor, as maps focus on the qualities, contours or criteria of roads, in ways that naturalize the man-made features that will be sensed by machines, including variations in traffic flows, rather than familiarity with the surrounding landscape, weather, or even road conditions.  The promises of reduced commute hours, expanded public transit lines, fewer fatalities, and an economy of passenger-friendly vehicles seem to depend on the “intelligence” of these maps, however, and on how a computer intelligence can provide an intelligent reading of an automatically sensed environment of other self-driving cars, presumably programmed to drive like humans, or at least to register their own motions.  While the licensing of map data will mean, in fact, the broadest ever generation and destruction of cartographical data–unless someone develops a deep interest in historical road conditions recorded in real-time–digital sensors and deep learning models are promised to save the day in rendering static maps finally obsolete.  But will the maps for self-driving cars be able to adequately interact with the cartographic intelligences of human drivers, or of the humans who will presumably also people the world and street intersections?

And what will even guarantee that the self-driving cars will not go off the roads?  The absence of human intelligence from the maps for self-driving cars creates a code-space that seems to depend on its interaction with human intelligence far more than its maps seem to register at first sight.  The simulated scenarios that have been created for such self-driving cars by engineers seem to seek to “provide a view of the world that a driver alone cannot access, seeing in every direction simultaneously, and on wavelengths that go far beyond the human senses,” but by nature depend on the ability to translate real-time scenarios in HD maps–as well as topological models–into the car’s actual course.

 

 

Waymo

 

For in promising to synthesize, compress and make available amazing amounts of spatial information and data sufficient to process the rapid increase of roadways that increasingly clog much of the inhabited world, they are maps for the age of the anthropocene, when ever-increasing spaces are being paved.  And although even after the arrival of promising “autonomous vehicles” from Tesla, which has introduced a new Autopilot feature able to maneuver in well-marked highways, and tests for urban driving by Uber, General Motors, and of course Google, the limited safety of relying only on sensors to navigate space in many areas, where vehicles are forced to integrate LIDAR, mid- and low-range radar, camera-based sensors, and road maps of real-time situations, and have difficulty calibrating road conditions and weather with the efficiency human drivers do.   The absence of a clear road map for their integration, however, is paralleled by the inability to synthesize contingent information in maps, which in their absence of selectivity offer oddly hyper-rich levels of information.

The notion of processing such comprehensive maps was far away when DARPA sent tout a call in 2003 inviting engineers to design self-driving cars that could navigate a one-hundred-and-forty-two-mile-long course in the desert, near Barstow, CA, across the desert to Prima, Nevada, without giving them a sense of its coordinates on a race-course filled with gullies, turns, rocks, switchbacks and obstacles–from train tracks to cacti–hoped to integrate GPS and sensors to create a car able to navigate space in as complete an image of road conditions as was possible.  If the rugged nature of these rigged-out vehicles recalled the first-run of a Mad Max film in their outsized nature paramilitary nature, designed as if to master landscape of any sort, they were so over-fitted with machinery were they with what seemed futuristic sensors that were tantamount to signage–

 

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Poster 619x316

 

–to seem to wrestle with the fundamental problem of mastering spatial information that the new generation of autonomous vehicles have placed front and center.

The top-down attempt of DARPA to stage a race of autonomous vehicles, was intended to keep soldiers out of harm’s way in a military context.  But the attempt to generate a new sort of military vehicles raised compelling questions of integrating a range of spatial signs in their apparatus of machine vision, laser range-finding data, and satellite imagery, but suffered from an inability to take in environmental information–no cars completed the course, as it was staged, and the vehicle traveling the furthest went only seven and a half miles.  Even in a course that was located in the desert–still the preferred site, given the lack of weather conditions and better kept up road surfaces, to test most self-driving cars to minimize unpredicted external influence–the relation of car to world was less easily negotiated than many thought.

While the results of the DARPA grand challenge wasn’t immediately successful, although the basis it set for future collaboration between machine-learning and automotive companies in notions of remote sensing.  It placed front and center the problem remains of how to establish more than a one-dimensional picture of the road ahead of the car to navigate the road ahead most easily.  And by 2007, the Urban Challenge, invited autonomous vehicles to navigate streets of an urban environment in Victorville, Calif., against moving traffic and obstacles and following traffic regulations, in ways that lifted a corner on the mappability of the future of driverless cars, as if to throw pasta against th ewall in the hopes tht some of it would stick.  Although the new starting point of self-driving cars on a network of readable roads, equipped with recognizable signage, remains the most profitable area for development, the machine-readable road maps eerily naturalize the parameters of the roads in their content, and absent humans from their surface.  Despite the recourse to satellite photography and attempts to benefit from aerial views, the notion of a map for the autonomous vehicle was barely conceived.  But in the almost fifteen years since, the maps that are being developed for self-driving cars have grown into an industry of their own, promising to orient cars to machine-readable records of the roadways in real-time.

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Sleeping Roads, Ancient Highways, and Paper Towns

What’s the significance of names on a map?  Do they register roads that belong to the territory or only reflect continued use?  What sort of authority does a mapped road, byway, or highway retain in common law–and for how long must it be recognized as a road?  The existence of place-names and routes on a map have become an increasingly contested way to preserve a sense of place, and the survival of the “sleeping roads” of Vermont, the “Class 4” roads that are not maintained by towns, even if some receive some limited maintenance, suggest a historical network of the past, still partly visible and indeed rarely used.

The survival of these sleeping roads are.threatened in an age of the division of the long predominantly rural state, as a  market of construction threatens to obscure local knowledge and a long-valued sense of place.  The deep sense of injustice in the prospect of loosing the legal status of “ancient” and long-pathways preserved in records of in local townships face possible obliteration in the legal memory as such unpaved roads–often more tacitly known than still used for commerce–are going to be reclassified.  Indeed, as the state’s legislature has decided to reclassify common law roads to homogenize property records across the state, the outburst of local mapping seems not an act of antiquarian obscurantism, but a defense of local knowledge in an age of globalism and satellite mapping, where few of the older roads might appear from the sort of satellite-based mapping systems on which we increasingly depend.  While many of the “class 4” roads might be sought out by Mountain Bikers, eager for off-road experiences, or back roads where they can snake around mountain farms, but only maintained if deemed necessary for the public good.

The plan for a massive reclassification of “ancient” highways on the books but actually dormant in much of the state of Vermont may be a pro-development land grab, but suggests that the struggle for designating once common lands as private property (and resistance to it) are waged on maps.  The recent promise to reclassify registered but unnamed byways in the state–a mass of roads which were at one time used or previously surveyed as common-law byways, but have since fallen out of use to different degrees–has unintentionally generated a set of local storms about public memory.  In a state where many current town roads remain unpaved, and many more have faded into the largely forested landscape.  The drive to reclassify the diversity of unpaved roads and common law byways once preserved in local jurisdictions reveals the rise of property development for whom the retention of old spatial classifications obfuscates the exchange of private lands.

The local resistance to such a reclassification of roads in the rural state, which has attracted its share of fierce defenders of the local rights of communities long granted precedents to federal or state law, make the proposed elimination of “Ancient Highways” from local law a matter of contention.  The proposed reclassification of a multiplicity of roads poses a problem of having ceased to reflect the sort of use of landscape that developers want to encourage and private home-owners want to ensure.  Given the shifting nature of land use in Vermont, where older houses are increasingly on the market, as smaller agricultural farms close and die out, a premium has developed for the clear definition of ownership without any liens or qualifications.  Hence the increasing tensions between local municipalities in the state and any move by state government to abolish roads they long oversaw.  In a sense, the increased interest in helping demand for fungible residential properties that can be sold without qualification have run up against the multiplicity of roads that have continued to remain on the books.

As the real estate market in Vermont seems poised to heat up in much of the state, and smaller towns face a demand for brisk sales and a large pool of properties arrive on the market, the state seeks to remove any obstacles to development or become notorious for arcane property laws, remapping the “ancient” roads of Vermont opts to treat them as ancient, and, far more than unpaved, not part of its future landscape.  Yet the quilt of county regulations of roads that existed for most of the eighteenth century and was retained in most local maps before World War II reflected a local landscape of counties and townships rarely challenged before the arrival of interstate federal highways across the state during the 1970s, erasing the varied paths, trails, and common-law roads, long overseen by local city Selectboards and regarded as parts of the local landscape.

 

A Quilt of Counties

 

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