Presentation on theme: "Driverless Cars Samuel Erb CS534, Spring 2013 IMAGE:"— Presentation transcript:
Driverless Cars Samuel Erb CS534, Spring 2013 IMAGE:
Brief History Driverless car history dates back to the 1930s with radio controlled vehicles Gets interesting in the 1980 Ernst Dickmanns, leading a team at Bundeswehr University Munich creates an autonomous 5-ton Mercedes-Benze van By 1986 it can drive itself and by 1987 it can handle speeds approaching 60mph His work lead up to a km trip where the car was driven 95% of the time autonomously
How did Ernst do it? Ernst Dickmanns is considered to be the pioneer of the autonomous car recursive estimation Ernst referred to using all available information to construct a complete picture as 4-D vision Kalman filter Error correcting filter based on prior knowledge and an expectation of errors & measurements have a Gaussian Distribution First used in the navigation computer on the Apollo missions Artificial saccadic movements We see the world with saccadic movements Their entire system was capable of physically focusing on important objects
Brief History, continued The 1990s were less than exciting. University funded research projects in the US & Italy effectively kept pace with Ernst.
Brief History, continued 2000s - DARPA Grand Challenge! Created due to a response to a congressional mandate that all US military ground vehicles are to be unmanned by – 1 st Grand Challenge, Mojave Desert, kind of a failure Furthest team, a Carnegie Mellon Universitys converted Humvee, made it 7.32 miles of the 150 mile course 2005 – 2 nd Grand Challenge, Mojave Desert, 5 finished! Winner was the now iconic red bull decorated Stanford University Stanley which did the 132 miles in just under 7 hours 2007 – 3 rd Grand Challenge, George Air Force Base, California Winner was Carnegie Mellon Universitys Boss with a time of 4 hours over the 60 mile urban course Since 2008 – autonomous commercial vehicles. Mainly used in mining operations.
Boss (Carnegie Mellon University) Winner of the 2007 DARPA Grand Challenge Multiple layers: Motion Planning – executes current motion goal Trajectory Generation – model-predictive trajectory generator On-Road Navigation – creates curve along center lane and aligns rear tires Zone Navigation – handles navigation when there are no lane markers (Anytime D*) Never makes assumptions about objects (moving/not moving).
Boss, continued Anytime D* A few variations, but main concept is iteratively finding better solutions over time (typically D* lite is used) 2 second overview Similar to A*, but can raise/lower estimated cost while being computed Starts from goal and works backwards, checking for estimation inconsistancies Can be modified in real-time as start moves
Boss, continued – errors observed
Future Many car models already include autonomous assists Within the next 3 years, many car manufacturers expect to have cars featuring steering, braking and lane guidance Within 5 years Google expects to release its technology Within the next 10 years many car manufacturers expect to have fully autonomous cars for sale!
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