Forum for Science, Industry and Business

Sponsored by:     3M 
Search our Site:

 

Self-driving cars for country roads

07.05.2018

Most autonomous vehicles require intricate hand-labeled maps, but MIT CSAIL's MapLite enables navigation with just GPS and sensors

Uber's recent self-driving car fatality underscores the fact that the technology is still not ready for widespread adoption. One reason is that there aren't many places where self-driving cars can actually drive. Companies like Google only test their fleets in major cities where they've spent countless hours meticulously labeling the exact 3D positions of lanes, curbs, off-ramps and stop signs.


Uber's recent self-driving car fatality underscores the fact that the technology is still not ready for widespread adoption. One reason is that there aren't many places where self-driving cars can actually drive. Companies like Google only test their fleets in major cities where they've spent countless hours meticulously labeling the exact 3D positions of lanes, curbs, off-ramps and stop signs.

Credit: MIT CSAIL

Indeed, if you live along the millions of miles of U.S. roads that are unpaved, unlit or unreliably marked, you're out of luck. Such streets are often much more complicated to map, and get a lot less traffic, so companies are unlikely to develop 3D maps for them anytime soon. From California's Mojave Desert to Vermont's White Mountains, there are huge swaths of America that self-driving cars simply aren't ready for.

One way around this is to create systems advanced enough to navigate without these maps. In an important first step, a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed MapLite, a new framework that allows self-driving cars to drive on roads they've never been on before without 3D maps.

MapLite combines simple GPS data that you'd find on Google Maps with a series of sensors that observe the road conditions. In tandem, these two elements allowed the team to autonomously drive on multiple unpaved country roads in Devens, Massachusetts, and reliably detect the road more than 100 feet in advance. (As part of a collaboration with the Toyota Research Institute, researchers used a Toyota Prius that they outfitted with a range of LIDAR and IMU sensors.)

"The reason this kind of 'map-less' approach hasn't really been done before is because it is generally much harder to reach the same accuracy and reliability as with detailed maps," says CSAIL graduate student Teddy Ort, who was a lead author on a related paper. "A system like this that can navigate just with on-board sensors shows the potential of self-driving cars being able to actually handle roads beyond the small number that tech companies have mapped."

The paper, which will be presented in May at the International Conference on Robotics and Automation (ICRA) in Brisbane, Australia, was co-written by MIT professor Daniela Rus and PhD graduate Liam Paull, who is now an assistant professor at the University of Montreal.

How it works

For all the progress that has been made with self-driving cars, their navigation skills still pale in comparison to humans'. Consider how you yourself get around: if you're trying to get to a specific location, you probably plug an address into your phone and then consult it occasionally along the way, like when you approach intersections or highway exits.

However, if you were to move through the world like most self-driving cars, you'd essentially be staring at your phone the whole time you're walking. Existing systems still rely heavily on maps, only using sensors and vision algorithms to avoid dynamic objects like pedestrians and other cars.

In contrast, MapLite uses sensors for all aspects of navigation, relying on GPS data only to obtain a rough estimate of the car's location. The system first sets both a final destination and what researchers call a "local navigation goal", which has to be within view of the car. Its perception sensors then generate a path to get to that point, using LIDAR to estimate the location of the road's edges. MapLite can do this without physical road markings by making basic assumptions about how the road will be relatively more flat than the surrounding areas.

"Our minimalist approach to mapping enables autonomous driving on country roads using local appearance and semantic features such as the presence of a parking spot or a side road," says Rus.

The team developed a system of models that are "parameterized", which means that they describe multiple situations that are somewhat similar. For example, one model might be broad enough to determine what to do at intersections, or what to do on a specific type of road.

MapLite differs from other map-less driving approaches that rely more on machine learning by training on data from one set of roads and then being tested on other ones.

"At the end of the day we want to be able to ask the car questions like 'how many roads are merging at this intersection?'" says Ort. "By using modeling techniques, if the system doesn't work or is involved in an accident, we can better understand why."

MapLite is still limited in many ways. It isn't yet reliable enough for mountain roads, since it doesn't account for dramatic changes in elevation. As a next step, the team hopes to expand the variety of roads that the vehicle can handle. Ultimately they aspire to have their system reach comparable levels of performance and reliability as mapped systems but with a much wider range.

"I imagine that the self-driving cars of the future will always make some use of 3D maps in urban areas," says Ort. "But when called upon to take a trip off the beaten path, these vehicles will need to be as good as humans at driving on unfamiliar roads they have never seen before. We hope our work is a step in that direction."

###

This project was supported in part by the National Science Foundation and the Toyota Research Initiative.

Media Contact

Rachel Gordon
rachelg@csail.mit.edu
617-823-5537

 @mit_csail

http://www.csail.mit.edu/ 

Rachel Gordon | EurekAlert!

Further reports about: 3D Artificial Intelligence Laboratory CSAIL GPS data Self-driving cars

More articles from Automotive Engineering:

nachricht The car of the future – sleeper cars and travelling offices too?
18.06.2018 | Fraunhofer-Institut für Arbeitswirtschaft und Organisation IAO

nachricht When your car knows how you feel
20.12.2017 | FZI Forschungszentrum Informatik am Karlsruher Institut für Technologie

All articles from Automotive Engineering >>>

The most recent press releases about innovation >>>

Die letzten 5 Focus-News des innovations-reports im Überblick:

Im Focus: First evidence on the source of extragalactic particles

For the first time ever, scientists have determined the cosmic origin of highest-energy neutrinos. A research group led by IceCube scientist Elisa Resconi, spokesperson of the Collaborative Research Center SFB1258 at the Technical University of Munich (TUM), provides an important piece of evidence that the particles detected by the IceCube neutrino telescope at the South Pole originate from a galaxy four billion light-years away from Earth.

To rule out other origins with certainty, the team led by neutrino physicist Elisa Resconi from the Technical University of Munich and multi-wavelength...

Im Focus: Magnetic vortices: Two independent magnetic skyrmion phases discovered in a single material

For the first time a team of researchers have discovered two different phases of magnetic skyrmions in a single material. Physicists of the Technical Universities of Munich and Dresden and the University of Cologne can now better study and understand the properties of these magnetic structures, which are important for both basic research and applications.

Whirlpools are an everyday experience in a bath tub: When the water is drained a circular vortex is formed. Typically, such whirls are rather stable. Similar...

Im Focus: Breaking the bond: To take part or not?

Physicists working with Roland Wester at the University of Innsbruck have investigated if and how chemical reactions can be influenced by targeted vibrational excitation of the reactants. They were able to demonstrate that excitation with a laser beam does not affect the efficiency of a chemical exchange reaction and that the excited molecular group acts only as a spectator in the reaction.

A frequently used reaction in organic chemistry is nucleophilic substitution. It plays, for example, an important role in in the synthesis of new chemical...

Im Focus: New 2D Spectroscopy Methods

Optical spectroscopy allows investigating the energy structure and dynamic properties of complex quantum systems. Researchers from the University of Würzburg present two new approaches of coherent two-dimensional spectroscopy.

"Put an excitation into the system and observe how it evolves." According to physicist Professor Tobias Brixner, this is the credo of optical spectroscopy....

Im Focus: Chemical reactions in the light of ultrashort X-ray pulses from free-electron lasers

Ultra-short, high-intensity X-ray flashes open the door to the foundations of chemical reactions. Free-electron lasers generate these kinds of pulses, but there is a catch: the pulses vary in duration and energy. An international research team has now presented a solution: Using a ring of 16 detectors and a circularly polarized laser beam, they can determine both factors with attosecond accuracy.

Free-electron lasers (FELs) generate extremely short and intense X-ray flashes. Researchers can use these flashes to resolve structures with diameters on the...

All Focus news of the innovation-report >>>

Anzeige

Anzeige

VideoLinks
Industry & Economy
Event News

Leading experts in Diabetes, Metabolism and Biomedical Engineering discuss Precision Medicine

13.07.2018 | Event News

Conference on Laser Polishing – LaP: Fine Tuning for Surfaces

12.07.2018 | Event News

11th European Wood-based Panel Symposium 2018: Meeting point for the wood-based materials industry

03.07.2018 | Event News

 
Latest News

NYSCF researchers develop novel bioengineering technique for personalized bone grafts

18.07.2018 | Life Sciences

Machine-learning predicted a superhard and high-energy-density tungsten nitride

18.07.2018 | Materials Sciences

Why might reading make myopic?

18.07.2018 | Health and Medicine

VideoLinks
Science & Research
Overview of more VideoLinks >>>