Bored at work? Here’s a Google-style digital map of the Roman Empire to play with

The heartland of the Roman Empire. Image: Screenshot of the Digital Map of the Roman Empire.

If you're anything like me, you'll have spent many years fretting over a single vexed question: What's the best route from Camulodunum to Korinion? Should you take the direct route via Verlamium? Or the more southerly one, through Londinium? It's a tricky one.

Well worry no longer – for the Roman Empire has finally joined the 21st century. This map is a sort of Google Maps of antiquity. It’s fully searchable, and comes with multiple zoom levels.

You can see the entire sweep of the empire, with its provinces marked out (you can click to expand the map):

Or you can zoom right in to see its heartland, complete with cemeteries (the tomb stones), villas (semi-circles) and temples (stars):

You can even search for specific places:

Sadly, it doesn't go down to street map level – though that’s probably more a reflection of the limits of the data than the limits of the cartographer’s ambition.


The map is the work of Johan Åhlfeldt, a researcher at Sweden's Lund University, who built it using sources including the  Barrington Atlas of the Greek and Roman World and the Pleides dataset. In all it has eight different zoom levels, with a ninth covering the regions (Italy, Greece and points east) where data is richest.

The Roman Empire, of course, was around for a while: there were emperors in the west for half a millennium, and the Roman Republic had been conquering territory well beyond Italy for a couple of centuries even before Augustus got his hands on power. Maps tend to change rather a lot over that many centuries.

But as Åhlfeldt explains here, his map doesn’t reflect a particular point in history:

“In a departure from the original Barrington Atlas and the Pleiades dataset, our digital map does not try to implement time periods when places are attested, nor does it speculate on the certainty (or otherwise) of locations: only precise locations from the Pleiades dataset can be rendered on the map. Nevertheless, since many places lacked precise coordinates and/or feature data, a good deal of effort has been made to improve the data.”

That said, the names and borders of Roman provices changed rather a lot of over time – best we can tell, the ones shown on Åhlfeldt's map date from the early 2nd century CE.

Oh, and my quandary about getting from Camulodunum (Colchester) to Korinion (Cirencester)?

It's a longer route if you head south, but the roads are better quality.

Next time you're in Roman Britain, you can thank me.

You can see the whole map here. Check it out.

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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.