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The Details Of Tesla’s FSD Full Self-Driving
A car that drives itself - what could be more natural? It seems easy enough; here's a description from Arthur C. Clarke in his 1976 novel Imperial Earth.
"I hope all those other cars are on automatic," he said anxiously.
Washington looked a little shocked. "Of course," he said. "It's been a criminal offense for at least a hundred years to drive manually on a public highway. But we still have occasional psychopaths to kill themselves and other people..."
The big car was slowing down, it's computer brain sensing an exit ahead. Presently it peeled off from the parkway, then speeded up again along a narrow road whose surface rapidly disintegrated into a barely visible grass-covered track. Washington took the steering lever just a second before the END AUTO warning started to flash on the control panel.
In actuality, though, it seems that there are a few problems to be worked through by engineers, to make sure that a Tesla car can see what we see.
As the beautiful old car cruised in almost perfect silence under the guidance of it's automatic controls, Duncan tried to see something of the terrain through which she was passing. The spaceport was 50 km from the city - no one had yet invented a noiseless rocket - and the four-lane highway bore a surprising amount of traffic. Duncan could count at least 20 vehicles of different types and even though they were all moving in the same direction, the spectacle was somewhat alarming.
Release notes for #FSDBeta V10.7 pic.twitter.com/BFFiQh7iww
— Dirty Tesla ☃ 🎄 (@DirtyTesla) December 19, 2021
Here's the list in text:
- Improved object attributes network to reduce false cut-in slowdowns by 50% and lane assignment error by 19%.
- Improved photon-to-control vehicle response latency by 20% on average.
- Expanded use of regenerative braking in Autopilot down to O mph for smoother stops and improved energy efficiency.
- Improved VRU (pedestrians, bicyclists, motorcycles, animals) lateral velocity error by 4.9% by adding more auto-labeled and simulated training examples to the dataset.
- Reduced false slowdowns for crossing objects by improved velocity estimates for objects at the end of visibility.
- Reduced false slowdowns by adding geometric checks to cross-validate lane assignment of objects.
- Improved speed profile for unprotected left turns when visibility is low. Added more natural behavior to bias over bike lanes during right turns.
- Improved comfort when yielding to jaywalkers by better modeling of stopping region with soft and hard deadlines.
- Improved smoothness for merge control with better modeling of merge point and ghost objects positioned at the edge of visibility.
- Improved overall comfort by enforcing stricter lateral jerk bounds in trajectory optimizer
- Improved short deadline lane changes through richer trajectory modeling.
- Improved integration between lead vehicle overtake and lane change gap selection.
- Updated trajectory line visualization.
Scroll down for more stories in the same category. (Story submitted 11/27/2021)
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