New MIT system enables autonomous driving on poorly mapped roads

New MIT system enables autonomous driving on poorly mapped roads

A new system from MIT, known as MapLite, enables self-driving cars to navigate using just GPS and sensors.

Self-driving systems are typically seen being tested in urban environments – locations that have been mapped and manually labelled to allow an AI to navigate the roads safely.

However, a team of researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), supported by the Toyota Research Institute, have rigged a Toyota Prius with LIDAR and IMU sensors and used CSAIL’s MapLite system to enable the vehicle to drive autonomously beyond city limits.

Rural roads often lack the painted lines, signs, and curbs that are used to map and autonomously navigate in urban areas, which is why huge stretches of the US are currently off-limits to self-driving vehicles, at least in their autonomous settings.

“The cars use these maps to know where they are and what to do in the presence of new obstacles, like pedestrians and other cars,” says Daniela Rus, director of CSAIL. “The need for dense 3D maps limits the places where self-driving cars can operate.”

MapLite uses sensors to including LIDAR to determine the the edges of the road (credit: MIT CSAIL).

MapLite autonomous driving

MapLite gets around this by doing away with the need for 3D maps, instead combining the sparse topological maps used for GPS navigation (for global navigation) with sensor-base perception (for local navigation).

The MapLite research paper, ‘Autonomous Vehicle Navigation in Rural Environments without Detailed Prior Maps’, describes how the system works:

First, a local navigation goal within the sensor view of the vehicle is chosen as a waypoint leading towards the global goal. Next, the local perception system generates a feasible trajectory in the vehicle frame to reach the waypoint while abiding by the rules of the road for the segment being traversed.

LIDAR is used to create a 3D point cloud, allowing the system to approximate the edges of the road, as well as a more reliable filtered estimate, meaning the road can be accurately detected over 100 feet ahead.

“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.

“A system like this that can navigate just with onboard sensors shows the potential of self-driving cars being able to actually handle roads beyond the small number that tech companies have mapped.”

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While the research is still in its infancy, as evidenced by the speed of travel in the featured video, it seeks to tackle a major obstacle in the way of the wide-scale adoption of autonomous vehicles. 3D mapping may be practical in urban areas, but another solution is needed for the countless miles of unfamiliar roads that link them.

Given that over one-third of US roads are unpaved and 65 percent lack reliable lane markings, self-driving cars need to become as capable as humans when driving on unfamiliar roads, to be widely useful.

LIDAR, used independently of 3D maps, does have its weaknesses. The current MapLite system is unable to account for dramatic changes in elevation, making it unsuited to mountain or cliff-top roads, but it’s an important first step that will see further development as autonomous vehicles explore new ground.

Arguably, however, the challenge of navigating rural roads safely may be one reason for aerial transport being more rapidly adopted in the near future, if air taxis simply have to navigate from one landing point to another.