Driverless Cars Helping Humankind Sprawl out Further

Driverless Cars Helping Humankind Sprawl out Further

In this interesting essay of Dr Timothy Hodgetts, Research Fellow in the Geopolitics of Wildlife Conservation, University of Oxford on the very actual subject of driverless cars helping humankind sprawl out further. How could this, sometime in the not so far future, affect our everyday life is concisely decortiquated in this article that is republished here with our thanks to The Conversation.

As far as the MENA region is concerned, this sprawling whilst comfortably seated behind a driverless car dash could be limited by the prevailing natural elements and that unlike those in more of clement, it will be confined to only those areas of sustainable life.

Driverless cars could see humankind sprawl ever further into the countryside

File 20170825 1020 1hemucp
BreezyInt / shutterstock

Dr Timothy Hodgetts, University of Oxford

Self-driving cars will change how we live, in all sorts of ways. But they won’t just affect us humans – the coming revolution in autonomous transport has significant implications for wildlife as well. Nature conservationists and planners need to think hard about the impact of driverless vehicles, most notably in terms of renewed urban sprawl.

In some ways, wider developments in automotive technology bode well for the environment. Electric cars will increasingly replace the internal combustion engine, and that should, in theory, reduce carbon emissions and health-afflicting air pollution.

Through minimising traffic jams, driverless cars may also reduce overall energy use. Unlike human drivers, computers can avoid the “concertina” effect of needless acceleration and braking that exacerbates congestion, and won’t be tempted to “rubberneck” when passing an accident. And, as autonomous vehicles aren’t restricted by human reaction times, it may make sense to increase speed limits for them on major inter-city routes.

So driverless cars promise a future of faster journey times with much reduced environmental impacts. They may even mean less wildlife roadkill. But it’s the very efficiency of driverless cars that poses a challenge for planners and conservationists. The threat is an unchecked increase in low-density urbanisation.

Driving into the countryside

Autonomous vehicles promise a future in which passengers are free to use their time productively (working, for example). And they can park themselves (or be part of a shared pool) which saves yet more time in the morning rush. Coupled with faster journey times, the incentives to live further out of town will increase significantly.

Sprawling cities like Los Angeles could cover an even larger area.
Melpomene / shutterstock

There are both push and pull factors at work here: sky-high residential prices in most cities push people away from urban centres while healthy environments and green living pull people towards the hinterlands. The limiting factor in suburban spread is often travel time, either by public or private means. Driverless cars fundamentally alter the equation.

Existing planning policies are based on our current transport systems. Green-belts, for example, are designed to reduce urban sprawl by restricting development within a buffer zone around an urban area. However, the reduced transport times offered by driverless cars make it easier to live outside the belt while still working inside. So these loops of green are in danger of becoming a thin layer in a sandwich of ever-spreading suburbanisation.

This is, of course, a familiar challenge since the rise of the automotive age in the 1940s. However, the solutions designed by planners have been calibrated for a human-driving automotive system – not for the supercharged future of driverless transport.

Other examples of planning protection for wildlife include nature reserves, national parks and (in the UK) “Areas of Outstanding Natural Beauty”. Such areas have either strict controls on development, or do not permit it at all. However, they are nice places to live in or nearby. The coming revolution in automotive journey times and the ability to work behind the (computer-driven) wheel will make living in such areas increasingly compatible with a commute to the nearest city.

Sick of sprawl

Natural habitats being lost entirely or splintered into ever-smaller fragments have long been understood as some of the primary causes of species extinctions across the world. Renewed urban sprawl threatens to increase the magnitude of both habitat loss and fragmentation. These threats are well known among conservationists, but there are differences of opinion on how best to respond.

For example, eco-modernists advocate a strategy of “land-sparing”, whereby human activities are concentrated into urban areas and vast tracts of land are set aside for nature. There are many cultural and ethical problems inherent in herding humans into cities, but the near-term planning issues posed by autonomous vehicles will exacerbate the challenge given they will boost demand to live in “unspared” lands.

Alternatively, some conservationists advocate “land-sharing”, in which human communities redesign the way we farm and live so as to co-exist with wildlife, cheek-by-jowl. Autonomous vehicles pose significant challenges for either approach, by supercharging the fragmentary effect of road systems.

Whichever approach is taken, we’ll need to redesign existing systems and policies to take account of the increased range that driverless transport facilitates. This may involve new zoning laws to protect wider areas of countryside than at present. It certainly requires further development of green infrastructure, habitat corridors and “greenways”.

This bridge in Banff National Park, Canada, was built specially for bears.
WikiPedant, CC BY-SA

It might also involve engineering solutions, especially given the fact that autonomous vehicles should be much more amenable to being driven underground. It is possible to imagine a future in which the famous bear bridges of Banff are tiny precursors to a vast programme in which rural highways are covered with forests of green. Retro-fitting roads into tunnels won’t be cheap, but it becomes easier when human drivers are taken out of the equation. Software drivers are less bothered by artificial light and more efficient at mitigating the congestion impact during construction.

The ConversationMuch conservation policy is based on planning for the world we live in now. Strategic conservation planning needs instead to take account of likely futures. And in a future of driverless cars, that is likely to result in the mega cities of the 20th century becoming the mega sprawls of the 21st. Unless, of course, planners and conservationists rise to this new challenge.

Dr Timothy Hodgetts, Research Fellow in the Geopolitics of Wildlife Conservation, University of Oxford

This article was originally published on The Conversation. Read the original article.

When will Robotics be applied to our Transportation

When will Robotics be applied to our Transportation

Following our article “The end of ever-rising consumption of Oil is in sight” here is something that is close and related to that, e.g. Self-driving cars.  It is about when will robotics be applied to our transportation modes and be made accessible to the public at large.
Automation and Artificial Intelligence that are obviously required in any self-driving vehicle have been in and out of our life for so many years that I personally cannot recall anytime without it either hearing and / or being talked about it, disserted on, etc.  As a matter of fact, there has never been in the technological world as much change as there is now and still is.
The prevalence of the combustion engine car has never been as much under question as it is now because of its direct impact on the environment. 
There is still strong belief however (as per confirmed forecasts) that the number of this type of cars worldwide will increase from less of today’s billion to something short of 2 billion by 2035 whilst that of the electric car would be from 0.1% to 6% of that figure.
McKinsey Automotive & Assembly in an article covering their insights produced an enlightening review of the on-going work in progress.  Here it is with our compliments to the team of authors.


Self-driving car technology: When will the robots hit the road?

By Kersten Heineke, Philipp Kampshoff, Armen Mkrtchyan, and Emily Shao

As cars achieve initial self-driving thresholds, some supporters insist that fully autonomous cars are around the corner. But the technology tells a (somewhat) different story.

The most recent people targeted for replacement by robots? Car drivers—one of the most common occupations around the world. Automotive players face a self-driving-car disruption driven largely by the tech industry, and the associated buzz has many consumers expecting their next cars to be fully autonomous. But a close examination of the technologies required to achieve advanced levels of autonomous driving suggests a significantly longer timeline; such vehicles are perhaps five to ten years away.

Mapping a technology revolution

The first attempts to create autonomous vehicles (AVs) concentrated on assisted-driving technologies (see sidebar, “What is an autonomous vehicle?,” for descriptions of SAE International’s levels of vehicle autonomy). These advanced driver-assistance systems (ADAS)—including emergency braking, backup cameras, adaptive cruise control, and self-parking systems—first appeared in luxury vehicles. Eventually, industry regulators began to mandate the inclusion of some of these features in every vehicle, accelerating their penetration into the mass market. By 2016, the proliferation of ADAS had generated a market worth roughly $15 billion.

Around the world, the number of ADAS systems (for instance, those for night vision and blind-spot vehicle detection) rose from 90 million units in 2014 to about 140 million in 2016—a 50 percent increase in just two years. Some ADAS features have greater uptake than others. The adoption rate of surround-view parking systems, for example, increased by more than 150 percent from 2014 to 2016, while the number of adaptive front-lighting systems rose by around 20 percent in the same time frame (Exhibit 1).

Exhibit 1

Both the customer’s willingness to pay and declining prices have contributed to the technology’s proliferation. A recent McKinsey survey finds that drivers, on average, would spend an extra $500 to $2,500 per vehicle for different ADAS features. Although at first they could be found only in luxury vehicles, many original-equipment manufacturers (OEMs) now offer them in cars in the $20,000 range. Many higher-end vehicles not only autonomously steer, accelerate, and brake in highway conditions but also act to avoid vehicle crashes and reduce the impact of imminent collisions. Some commercial passenger vehicles driving limited distances can even park themselves in extremely tight spots.

But while headway has been made, the industry hasn’t yet determined the optimum technology archetype for semiautonomous vehicles (for example, those at SAE level 3) and consequently remains in the test-and-refine mode. So far, three technology solutions have emerged:

  • Camera over radar relies predominantly on camera systems, supplementing them with radar data.
  • Radar over camera relies primarily on radar sensors, supplementing them with information from cameras.
  • The hybrid approach combines light detection and ranging (lidar), radar, camera systems, and sensor-fusion algorithms to understand the environment at a more granular level.

The cost of these systems differs; the hybrid approach is the most expensive one. However, no clear winner is yet apparent. Each system has its advantages and disadvantages. The radar-over-camera approach, for example, can work well in highway settings, where the flow of traffic is relatively predictable and the granularity levels required to map the environment are less strict. The combined approach, on the other hand, works better in heavily populated urban areas, where accurate measurements and granularity can help vehicles navigate narrow streets and identify smaller objects of interest.