Photo: Sterling Anderson for MIT Robotic Mobility Group

There are autonomous cars, and there are drivers’ cars. Now we have something in the middle. Sterling Anderson and Karl Iagnemma of MIT have created a semi-autonomous driving system that gives drivers full control of the vehicle, but kicks when the car gets too close to another object. This sounds like the adaptive cruise control found in expensive Mercedes-Benzes, but this software is much more nuanced and ambitious than anything on the road.

Anderson, a Ph.D. student, and Iagnemma, principle research scientist at the MIT Robotic Mobility Group, designed the algorithm with Quantum Signal LLC, a small technology developer in Michigan, where most of the testing was conducted. Unlike with fully autonomous vehicles, the sensors and software evaluate the environment with constant adjustments to boundaries. A self-parking car, for example, works with a planned path of travel among static obstacles, a much simpler task than adapting to constant variables, including a human driver.

There are certain advantages to synergy. “Automation excels at responding quickly and precisely to well-defined or repetitive control objectives; humans tend to make more mistakes as the frequency and complexity of the control task increase,” Anderson says. In the opening to their paper for the 2012 Intelligent Vehicles Symposium, Anderson elaborates: “Conversely, humans have the unique ability to detect and contextualize patterns and new information, reason inductively, and adapt to new modes of operation, whereas automation typically struggles at these tasks.”

Anderson and Iagnemma’s program evaluates surrounding constraints, then selects which surroundings need to be acted upon. Using data from a front-mounted camera and a laser range-finder, the algorithm picks out “homotopies,” or safe zones, then incorporates them into a map divided into triangles, with the edge of each shape representing a lane boundary or tree. It accounts for the vehicle’s limits, such as steering and tire friction, plans an escape to correct the car, then relinquishes control.

They’ve conducted over 1,200 trials using a Kawasaki Mule outfitted with LIDAR, inertial gauges, GPS, a Linux PC, and actuators for steering, acceleration and braking. Drivers sat before a computer monitor with video feeding from the Mule, and drove as normal with a virtual steering wheel and pedals. From these test runs, the software and measurers reduced chances of an accident by 75 percent. They attribute the remaining 25 percent to their LIDAR reader’s 9.8-foot blind spot.

The end goal would be to squeeze all of this into a dash-mounted smartphone. Using a quality phone’s high-resolution camera, accelerometers, and gyroscope, the software could gather all the necessary data to steer a driver away from a potential accident, making divine robotic intervention an app away.

wiredautopia?d=cGdyc7Q-1BI wiredautopia?i=xo9QCRAp67U:nN7lt3lUC6k:V_sGLiPBpWU wiredautopia?i=xo9QCRAp67U:nN7lt3lUC6k:gIN9vFwOqvQ wiredautopia?d=yIl2AUoC8zA

from Wired: Autopia