Uber and Google race against car firms to map the world's cities

2019-02-27 06:14:00

Ford/Civil maps By Hal Hodson IT’S a 4-hour drive from Pittsburgh to Detroit – but there’s an app for that. You punch the destination into your phone and a driverless car soon swings to a stop next to you. You jump in and it whisks you north-west towards the I-80 on-ramp. But as you merge with the highway traffic, the car pipes up: “This car runs on the Uber network, which does not cover Detroit. I cannot take you to your final destination. You will be dropped at an appropriate interchange point.” The way things are going, this could be the short-term prospect for driverless cars. The companies chasing a future full of autonomous vehicles are each creating a closed system in which their cars will work, but their competitors’ won’t – and it’s all to do with maps. “The maps need to be there for the autonomous cars to be able to do what they need to do“ Driverless cars carry many different kinds of sensors – including cameras, lidar and radar – but they are not capable of fully understanding what they see. They may be able to steer themselves around obstacles and brake to avoid collisions, but can have trouble reading unfamiliar objects in the way humans can. For example, before an autonomous car approaches a junction, it needs to know exactly where the traffic light will be. Because of this, driverless cars need highly detailed 3D maps of the roads they are to navigate. These are not top-down charts like you get from a satnav or Google Maps, but representations of street layouts and roadside infrastructure like barriers and traffic lights – plus information about where other cars are likely to be. Maps for driverless cars are like railways for trains, says John Ristevski at Nokia Growth Partners in Palo Alto, California. “The map needs to be there for the autonomous car to be able to do what it needs to do.” Companies are fighting to build their own version of such maps, using a variety of tactics. Last week, Uber hired Tesla’s head of mapping. Traditional car makers like Ford and Toyota are scrambling to take advantage of the millions of vehicles they have on the road already to harvest large volumes of data. Newcomers like Uber and Google are relying on their prowess with data science to give them an edge. All of them have customised mapping vehicles crawling the roads of their target areas, trying to get ahead. Creating those maps for a relatively small built-up area like a mid-sized city is not hard. “I think San Francisco has about 2000 kilometres of major roads,” says Ristevski. “You can map that with one high resolution mapping vehicle in about two weeks.” But extending those maps across larger urban environments – and eventually whole countries – will be painstaking, expensive work. It’s therefore no coincidence that the first driverless taxi service – announced in August – is launching in the tiny city-state of Singapore. The company behind it, a spin-off from the Massachusetts Institute of Technology called nuTonomy, mapped all of Singapore’s streets by driving around with a lidar scanner, says CEO Karl Iagnemma. Google, Uber, Ford and others are targeting a few different cities in the US with their mapping vehicles. Google is mapping a swathe of Silicon Valley around its headquarters in Mountain View, California; Ford is mapping the university town of Ann Arbor, Michigan; and Uber is mapping Pittsburgh. This month Uber announced that people requesting a ride via its app in Pittsburgh might now find themselves picked up by a driverless car – with a human driver on standby. But these companies may soon hit a stumbling block. Each one is building proprietary maps that only work with the sensors in their own cars. At the moment, the driverless cars that Uber is testing in Pittsburgh cannot run on Ford’s map in Michigan, for example. The maps are incompatible, like railway networks that operate on different gauges. “It’s a patchwork,” says Ristevski. However, Sanjay Sood at Chicago-based mapping company HERE – which was bought by a consortium of German car makers in 2015 – is not too worried. There is bound to be fragmentation early on, he says. But that will change. “There’s going to have to be some standardisation,” says Sood. Whether that is in the format of the maps themselves or the sensors and software that drive the cars remains to be seen. “It’s super early,” he says. “The reason you’re not seeing standards is that we’re still in the research and development stages.” “A car has far more computational power than a phone and much better sensors“ The dark horse in the map wars is Tesla. Elon Musk’s electric car company currently has 140,000 cars on the road around the world. Some models have an autopilot mode – in which the car can drive itself along relatively easy stretches of road as long as a human driver is ready to take over at any moment – but none are fully autonomous. However, the cars are still fitted with sensors that are needed for the autopilot feature, and all the data they gather is beamed back to Tesla. As the first company to put a data-gathering sensor network on thousands of public roads, Tesla could have its hands on data for far more locations than any other car company. But Tesla’s lead might not last long. Toyota plans to include the sensors required for autonomous driving in all of its new cars in 2017. These millions of vehicles won’t be autonomous themselves, but will gather the data needed for Toyota to build its own maps. To deal with this vast amount of information, Toyota is also building a data centre in Plano, Texas. Whoever wins, the maps on which driverless cars run are going to end up processing vast amounts of data beyond that needed for the cars to drive. They might include the location of hordes of pedestrians, roadworks, black ice and other weather hazards, for example (see “Eyes on the road“). “If you look at technology today the mobile phone is seen as this powerful device,” says Sood. “But 90 per cent of the time it’s in your pocket, not facing the world. A car has more computational power than a phone, and much better sensors.” As well as getting us from A to B, driverless cars could become a new platform for collecting data about the world, says Sanjay Sood at mapping company HERE in Chicago. Fitted with barometers and thermometers, a network of cars could deliver high quality local weather predictions, for example. Their cameras and accelerometers could also be used to monitor the state of the road and other infrastructure. If the car network spots a problem, it can mark the exact location on a map so local authorities can more easily fix it. Sood says maps will even include real-time data on where airbags have been deployed, or where emergency braking incidents happen – gold for those trying to sell insurance based on how cars are driven. This article appeared in print under the headline “The new cartographers” More on these topics: