the car drives along that road, the map will automatically be improved.
“Valid base data is created the first time that a route is driven. If, say, two more trips are taken along the same route, the quality of the map will be brought up to a very high level,” explains Christoph Keller, who also works on vehicle tracking in Daimler’s advance development unit. Another advantage is that the map will grow as time goes on – whenever the car drives along a road it has never been on before it will create the relevant map data.
What sounds relatively straightforward actually poses numerous technical challenges. “After all, you won’t just get a good map simply because you have a lot of information,” says Keller. The data has to be processed in the right way in order to produce the level of detail that’s required.” Individual algorithms, executed by computer software, produced the desired result: a car programmed to learn by itself is now able to create its own high-precision digital map. This is important for autonomous driving but also for optimising routine trips from within the data-gathering car, which is seen as an additional convenience feature.
ALL FOR ONE, ONE FOR ALL
The next stages of development are already mapped out. The only practical way to guarantee efficiency and accuracy at all times is for vehicles to collaborate – i.e. to not create the maps individually but to do so centrally, using a multitude of pooled data that will then be processed and made available to all participating vehicles. “The vehicles will send the route data via mobile networks to a central computer, known as a backend because of how it works behind the scenes. This backend compiles all this data into a digital map that then finds its way back to the vehicles via the internet,” explains Haueis.
There are many advantages to this method. The maps used by the self-driving cars will have the highest level of detail and will always be up to date. And key resources such as computing power and learning algorithms will be centralised rather than having to be accommodated in the vehicles themselves. “For this solution too, the art lies in creating the necessary programming codes – as we’ve said, simply having a lot of data does not guarantee a high-quality result. What matters is how it’s processed.” The developers have not quite reached the point where they are satisfied with the backend coding. But they are close: The future does, after all, usually arrive faster than expected.
The Bertha Benz drive in 2013
The 100km drive from Mannheim to Pforzheim followed the same route taken by automotive pioneer Bertha Benz, who in 1888 made the first long-distance journey by motor car.
In mid-2013 the Mercedes-Benz S 500 INTELLIGENT DRIVE research car drove itself across country and in towns. On the busy roads of the 21st century the self-driving S-Class was able to handle highly complex situations – with traffic lights, roundabouts, pedestrians, cyclists and trams.
This game-changing success was unusual because it did not require expensive specialist technology to be developed, but was brought to fruition using equipment that is nearly production ready and is similar to what is already widely available in current Mercedes-Benz models.
The project represents a milestone on the journey from the car that moves by itself (automotive) to the car that drives by itself (autonomous).
via : http://zumzumauto.blogspot.com/
“Valid base data is created the first time that a route is driven. If, say, two more trips are taken along the same route, the quality of the map will be brought up to a very high level,” explains Christoph Keller, who also works on vehicle tracking in Daimler’s advance development unit. Another advantage is that the map will grow as time goes on – whenever the car drives along a road it has never been on before it will create the relevant map data.
What sounds relatively straightforward actually poses numerous technical challenges. “After all, you won’t just get a good map simply because you have a lot of information,” says Keller. The data has to be processed in the right way in order to produce the level of detail that’s required.” Individual algorithms, executed by computer software, produced the desired result: a car programmed to learn by itself is now able to create its own high-precision digital map. This is important for autonomous driving but also for optimising routine trips from within the data-gathering car, which is seen as an additional convenience feature.
ALL FOR ONE, ONE FOR ALL
The next stages of development are already mapped out. The only practical way to guarantee efficiency and accuracy at all times is for vehicles to collaborate – i.e. to not create the maps individually but to do so centrally, using a multitude of pooled data that will then be processed and made available to all participating vehicles. “The vehicles will send the route data via mobile networks to a central computer, known as a backend because of how it works behind the scenes. This backend compiles all this data into a digital map that then finds its way back to the vehicles via the internet,” explains Haueis.
There are many advantages to this method. The maps used by the self-driving cars will have the highest level of detail and will always be up to date. And key resources such as computing power and learning algorithms will be centralised rather than having to be accommodated in the vehicles themselves. “For this solution too, the art lies in creating the necessary programming codes – as we’ve said, simply having a lot of data does not guarantee a high-quality result. What matters is how it’s processed.” The developers have not quite reached the point where they are satisfied with the backend coding. But they are close: The future does, after all, usually arrive faster than expected.
The Bertha Benz drive in 2013
The 100km drive from Mannheim to Pforzheim followed the same route taken by automotive pioneer Bertha Benz, who in 1888 made the first long-distance journey by motor car.
In mid-2013 the Mercedes-Benz S 500 INTELLIGENT DRIVE research car drove itself across country and in towns. On the busy roads of the 21st century the self-driving S-Class was able to handle highly complex situations – with traffic lights, roundabouts, pedestrians, cyclists and trams.
This game-changing success was unusual because it did not require expensive specialist technology to be developed, but was brought to fruition using equipment that is nearly production ready and is similar to what is already widely available in current Mercedes-Benz models.
The project represents a milestone on the journey from the car that moves by itself (automotive) to the car that drives by itself (autonomous).
via : http://zumzumauto.blogspot.com/
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