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New Technique Controls Autonomous Vehicles in Extreme Conditions

Researchers from Georgia Institute of Technology have devised a new way to help keep a driverless vehicle under control as it maneuvers at the edge of its handling limits. The approach could help make future autonomous cars safer under hazardous road conditions. The researchers assessed the new technology by racing, sliding, and jumping one-fifth-scale, fully autonomous auto-rally cars at the equivalent of 90 mph. The technique uses advanced algorithms and onboard computing, combined with installed sensing devices, to increase vehicular stability while maintaining performance. Traditional robotic-vehicle techniques use the same control approach whether a vehicle is driving normally or at the edge of roadway adhesion. The Georgia Tech method – known as model predictive path integral control (MPPI) – was developed specifically to address the non-linear dynamics involved in controlling a vehicle near its friction limits.

Topics:
Automation Automotive Computers Electronics & Computers Internet of Things Motion Control Robotics Sensors Transportation

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    Transcript

    00:00:00 The exciting thing about this technology that we have is the ability for the car to perform what are known as progressive maneuvers. So during turns, for example, the car is literally sliding around the turn. It's not maintaining its complete contact with the road surface. And yet in spite of that sliding maneuver, which gives us a lot of speed in that turns, the car

    00:00:26 is actually stable and safe in navigating this particular feature of our track. The algorithms that we have developed are able to project into the future what the vehicle is going to do in the next three or four or five seconds. And generate approximately 2,000 or 3,000 possibilities of what's going to happen. And based on these possibilities, it chooses the best one.

    00:00:49 And this can be done very, very fast. I think we're calculating about 2000 different possibilities every 50 milliseconds. This algorithm will have a broad impact. It doesn't have to be within aggressive driving. It's can be within any domain, with autonomy. We can think about tasks that relate to locomotion. Manipulation. You have a robot, and you want to be able to manipulate objects.

    00:01:14 Everything that involves, essentially, controlling of a system. That is very non-linear. That is very uncertain. You don't know exactly how it's going to react.