I'm joined by James Douma as we take a look at the most recent Tesla FSD Beta enhancements.
Andrej Karpathy interview on @Lex Fridman:
Tesla FSD Beta 10.69.3 release notes:
– Upgraded the Object Detection network to photon count video streams and retrained all criteria with the latest autolabeled datasets (with an unique focus on low presence circumstances).
Enhanced the architecture for much better accuracy and latency, greater recall of far lorries, lower speed error of crossing cars by 20%, and improved VRU precision by 20%.
– Transformed the VRU Speed network to a two-stage network, which minimized latency and enhanced crossing pedestrian speed mistake by 6%.
– Converted the Non VRU Attributes network to a two-stage network, which reduced latency, decreased inaccurate lane assignment of crossing cars by 45%, and minimized incorrect parked forecasts by 15%.
– Reformulated the autoregressive Vector Lanes grammar to enhance accuracy of lanes by 9.2%, recall of lanes by 18.7%, and recall of forks by 51.1%. Includes a full network upgrade where all components were re-trained with 3.8 x the quantity of information.
– Included a brand-new "roadway markings" module to the Vector Lanes neural network which enhances lane geography error at crossways by 38.9%.
– Upgraded the Occupancy Network to line up with roadway surface rather of ego for improved detection stability and improved recall at hill crest.
– Minimized runtime of prospect trajectory generation by roughly 80% and enhanced smoothness by distilling an expensive trajectory optimization treatment into a lightweight coordinator neural network.
– Improved decision producing brief deadline lane modifications around gores by richer modeling of the trade-off in between going off-route vs trajectory needed to drive through the gore area
– Decreased incorrect slowdowns for pedestrians near crosswalk by using a much better design for the kinematics of the pedestrian
– Included control for more exact object geometry as detected by general tenancy network.
– Improved control for cars eliminating of our preferred path by better modeling of their turning/ lateral maneuvers thus avoiding abnormal slowdowns
– Improved longitudinal control while offsetting around fixed challenges by browsing over feasible automobile motion profiles
– Improved longitudinal control smoothness for in-lane cars during high relative velocity scenarios by likewise considering relative acceleration in the trajectory optimization
– Decreased finest case things photon-to-control system latency by 26% through adaptive planner scheduling, restructuring of trajectory selection, and parallelizing understanding compute. This enables us to make quicker choices and enhances reaction time.
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