New framework allows self-driving cars on new roads without 3D maps
Daniela Rus, Director of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), and colleagues have developed MapLite, a framework designed to allow self-driving cars to drive on roads they have never been on before without 3D maps.
MapLite combines simple GPS data that is found on Google Maps with a series of sensors that observe the road conditions. Together, these two elements allowed the team to autonomously drive on unpaved country roads in Devens, MA, and reliably detect the road more than 100 ft in advance. For the testing, 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,” said CSAIL graduate student Teddy Ort, who was a lead author on a related paper about the system. “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 Ort, Rus, and PhD graduate Liam Paull, who is now an assistant professor at the University of Montreal.
According to the researchers, 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 also a “local navigation goal,” which must be within view of the car. Its perception sensors then generate a path to reach that point, and LiDAR is used to estimate the location of the road’s edges. MapLite can accomplish this without physical road markings by making basic assumptions about the road being relatively flatter than 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,” said 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.
“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?’” commented Ort. “By using modeling techniques, if the system doesn’t work or is involved in an accident, we can better understand why.”
MapLite still has some limitations: It isn’t yet reliable enough for mountain roads, for instance, since it doesn’t account for large changes in elevation. Next, the team hopes to expand the variety of roads that the vehicle can handle. The goal is to have the 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,” said 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 partially supported by the National Science Foundation and the Toyota Research Initiative.