“The change of use can add to the history of a place, rather than diminishing it”

BBC Television Centre, 2009: the site has since been sold to private developers. Image: PanHard/Wikimedia Commons.

The news last week that the BBC is to close Maida Vale Studios and relocate live music to Stratford in East London was received with exactly the kind of enthusiasm long time media watchers have come to expect from this kind of thing – with words like “disgusting” and “misguided”, and pleas for the preservation of our cultural heritage. Hashtag campaigns and requests for listing have been duly launched into the ether.

The responses from musicians and music fans echoed those from TV historians to the partial conversion of the BBC's Television Centre to private flats over recent years, with occasional outbursts of “I can't bear to look at it” and “It makes me feel sad when I go past” continuing to this day.

Now I'm not an architect, property developer, TV producer or internationally renowned rock guitarist, sadly; so I can't speak to whether either of these land deals are particularly good value, or the claims and counter-claims as to the long term viability of the old facilities and their new replacements. Equally, there's a whole separate argument about the fact that such London buildings are usually converted into high-end oligarch hives that are at best a symptom and at worse a driver of inequalities within our cities and society as a whole.

What I do question, though, is the idea that preserving heritage in our built environment requires continuity of use. There is of course an undeniable buzz for people working in a particular creative industry to occupy the spaces and walk the corridors their predecessors did, to be part of a history. When change must come, there's also a case for excellent examples of workplaces to be preserved as museums or heritage sites.

But the impulse to freeze a building in its current use, fixing its purpose like the glue-wielding bad guy in The Lego Movie, cuts against the city as a living, evolving place that changes with the requirements of its population and industries.


More than that: it’s through allowing changes of use, by preserving historic facades and putting up plaques but by allowing the buildings to be reshaped to contemporary needs, that history accumulates in the architecture of our older cities.

I live in Exeter, I used to live in London, and, when I was young, my favourite city near to my home town was York. All three cities date back to before Roman times, and are places where the medieval has been partially over-written by the eras that followed, with new development filling the spaces left by fire and war and other disasters. As the commercial areas of a city expand, old domestic dwellings find themselves reshaped into business properties, while further from these commercial centres former places of work become domestic properties. Hospitals become restaurants, old houses become shops, central tenements become office space and, yes, the factories and warehouses and studios of industries that have collapsed or moved on are split into apartments.

At worst these changes of use can feel like a crushing of the imagination. A place of once fervent worship might deserve better than becoming a Wetherspoons. We do not respect the toil and horrors that our historic docks represent by divvying up the buildings into cute riverside apartments with high price tags.

At best, though, there's a pleasure in coming across a building that has changed use over the centuries and decades; that bears a unique character from having spaces that bear the marks of previous use; that has quirks of layout that you wouldn't find in a building designed precisely for its current requirements. The change of use can add to the history of a place, rather than diminishing it.

The preservation of old signage, commemorative plaques and clues in street and square names all contribute to the idea that a city has a long, changing history. The fact that new uses are found for old buildings, that we can remake our buildings for a new use rather than just demolishing them and starting over, preserves history in a different way to heritage centres and museums. It weaves the past into the present, creating a sense of historic continuity that is layered and evolving. The separation between the preserved past and the under-construction future is dissolved – and we can see ourselves within a city's history rather than simply observing it.

 
 
 
 

Just like teenagers, self-driving cars need practice to really learn to drive

A self-driving car, of unknown level of education. Image: Grendelkhan/Flickr/creative commons.

What do self-driving cars and teenage drivers have in common?

Experience. Or, more accurately, a lack of experience.

Teenage drivers – novice drivers of any age, actually – begin with little knowledge of how to actually operate a car’s controls, and how to handle various quirks of the rules of the road. In North America, their first step in learning typically consists of fundamental instruction conveyed by a teacher. With classroom education, novice drivers are, in effect, programmed with knowledge of traffic laws and other basics. They then learn to operate a motor vehicle by applying that programming and progressively encountering a vast range of possibilities on actual roadways. Along the way, feedback they receive – from others in the vehicle as well as the actual experience of driving – helps them determine how best to react and function safely.

The same is true for autonomous vehicles. They are first programmed with basic knowledge. Red means stop; green means go, and so on. Then, through a form of artificial intelligence known as machine learning, self-driving autos draw from both accumulated experiences and continual feedback to detect patterns, adapt to circumstances, make decisions and improve performance.

For both humans and machines, more driving will ideally lead to better driving. And in each case, establishing mastery takes a long time. Especially as each learns to address the unique situations that are hard to anticipate without experience – a falling tree, a flash flood, a ball bouncing into the street, or some other sudden event. Testing, in both controlled and actual environments, is critical to building know-how. The more miles that driverless cars travel, the more quickly their safety improves. And improved safety performance will influence public acceptance of self-driving car deployment – an area in which I specialise.

Starting with basic skills

Experience, of course, must be built upon a foundation of rudimentary abilities – starting with vision. Meeting that essential requirement is straightforward for most humans, even those who may require the aid of glasses or contact lenses. For driverless cars, however, the ability to see is an immensely complex process involving multiple sensors and other technological elements:

  • radar, which uses radio waves to measure distances between the car and obstacles around it;
  • LIDAR, which uses laser sensors to build a 360-degree image of the car’s surroundings;
  • cameras, to detect people, lights, signs and other objects;
  • satellites, to enable GPS, global positioning systems that can pinpoint locations;
  • digital maps, which help to determine and modify routes the car will take;
  • a computer, which processes all the information, recognising objects, analysing the driving situation and determining actions based on what the car sees.

How a driverless car ‘sees’ the road.

All of these elements work together to help the car know where it is at all times, and where everything else is in relation to it. Despite the precision of these systems, however, they’re not perfect. The computer can know which pictures and sensory inputs deserve its attention, and how to correctly respond, but experience only comes from traveling a lot of miles.

The learning that is occurring by autonomous cars currently being tested on public roads feeds back into central systems that make all of a company’s cars better drivers. But even adding up all the on-road miles currently being driven by all autonomous vehicles in the U.S. doesn’t get close to the number of miles driven by humans every single day.

Dangerous after dark

Seeing at night is more challenging than during the daytime – for self-driving cars as well as for human drivers. Contrast is reduced in dark conditions, and objects – whether animate or inanimate – are more difficult to distinguish from their surroundings. In that regard, a human’s eyes and a driverless car’s cameras suffer the same impairment – unlike radar and LIDAR, which don’t need sunlight, streetlights or other lighting.

This was a factor in March in Arizona, when a pedestrian pushing her bicycle across the street at night was struck and killed by a self-driving Uber vehicle. Emergency braking, disabled at the time of the crash, was one issue. The car’s sensors were another issue, having identified the pedestrian as a vehicle first, and then as a bicycle. That’s an important distinction, because a self-driving car’s judgments and actions rely upon accurate identifications. For instance, it would expect another vehicle to move more quickly out of its path than a person walking.


Try and try again

To become better drivers, self-driving cars need not only more and better technological tools, but also something far more fundamental: practice. Just like human drivers, robot drivers won’t get better at dealing with darkness, fog and slippery road conditions without experience.

Testing on controlled roads is a first step to broad deployment of driverless vehicles on public streets. The Texas Automated Vehicle Proving Grounds Partnership, involving the Texas A&M Transportation Institute, University of Texas at Austin, and Southwest Research Institute in San Antonio, Texas, operates a group of closed-course test sites.

Self-driving cars also need to experience real-world conditions, so the Partnership includes seven urban regions in Texas where equipment can be tested on public roads. And, in a separate venture in July, self-driving startup Drive.ai began testing its own vehicles on limited routes in Frisco, north of Dallas.

These testing efforts are essential to ensuring that self-driving technologies are as foolproof as possible before their widespread introduction on public roadways. In other words, the technology needs time to learn. Think of it as driver education for driverless cars.

People learn by doing, and they learn best by doing repeatedly. Whether the pursuit involves a musical instrument, an athletic activity or operating a motor vehicle, individuals build proficiency through practice.

The ConversationSelf-driving cars, as researchers are finding, are no different from teens who need to build up experience before becoming reliably safe drivers. But at least the cars won’t have to learn every single thing for themselves – instead, they’ll talk to each other and share a pool of experience.

Johanna Zmud, Senior Research Scientist, Texas A&M Transportation Institute, Texas A&M University .

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