Researchers at the Georgia Institute of Technology are developing control algorithms that enable small-scale autonomous cars to race around dirt tracks at high speeds.
The cars use real-time onboard sensing and processing to maximize their speeds while remaining stable and under control.
AutoRally, their electrically-powered research platform, features a global-positioning system, an inertial measurement unit, wheel encoders, a pair of fast video cameras, and a quad-core i7 computer with a Nvidia GTX 750ti graphical-processing unit (GPU) and 32 gigabytes of random-access memory. AutoRally can calculate an optimized trajectory from the weighted average of 2,560 different trajectory possibilities, all simulated in parallel on the onboard GPU. Each of the trajectories represent the oncoming 2.5 seconds of vehicle motion, and AutoRally recomputes this optimization process 60 times every second.
A test of the cars powering around a dirt track shows most crashes happened due to either software crashes and not the algorithm itself, or the vehicle having trouble adapting to changes in the track surface.
The research could help prepare self-driving cars to handle potentially dangerous driving conditions.
From IEEE Spectrum
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