The development of self-driving vehicles is in full swing, but there are bumps in the road ahead.
Companies including Google are testing full fleets of autonomous vehicles, but one of the major obstacles is that self-driving systems require maps of their environments in order to avoid objects, hazards, and to navigate safely.
Driving-related objects, including stop signs, pedestrian crossings, curbs, and more must be mapped in order for self-driving vehicles to act in an appropriate manner on our roads.
This requirement alone has limited self-driving car pilots to the major cities and towns in which technology firms and automakers are performing tests and investing in the creation of full 3D maps.
However, researchers from the Massachusetts Institute of Technology (MIT) hope that a new navigation system will free autonomous vehicles from city streets.
There are millions of miles of road in the United States alone which are unpaved, unlit, unreliably marked, and in rural and remote areas. These factors prevent self-driving cars from being suitable to drive there, but MIT's MapLite is a system which may allow these vehicles to drive on new and unexplored roads.
Uber's fatal accident[1] during self-driving car tests forced both technology firms and regulators to take a step back and reassess how ready autonomous vehicles truly are for our roads.
While self-driving cars rely on constant map scanning and visual algorithms to stay out of collisions and accidents, MIT's framework focuses on more "human" elements of navigation.
The academic institution's Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed the framework[2], which replaces 3D maps with GPS data and sensors.
MapLite uses the same GPS data you